diff --git a/.eslintrc b/.eslintrc new file mode 100644 index 0000000..b995a91 --- /dev/null +++ b/.eslintrc @@ -0,0 +1,70 @@ +{ + "extends": [ + "plugin:@typescript-eslint/recommended", + "plugin:import/typescript", + "standard" + ], + "env": { + "es6": true, + "browser": true + }, + "plugins": [ + "svelte3" + ], + "parserOptions": { + "ecmaVersion": 12, + "sourceType": "module" + }, + "overrides": [ + { + "files": ["*.svelte"], + "processor": "svelte3/svelte3", + "rules": { + "import/first": "off", + "import/no-duplicates": "off", + "import/no-mutable-exports": "off", + "import/no-unresolved": "off" + } + } + ], + "rules": { + "object-curly-spacing": ["error", "always"], + "prefer-template": "error", + "no-useless-constructor": "off", + "import/extensions": 0, + "import/no-extraneous-dependencies": 0, + "import/prefer-default-export": 0, + "import/first": ["error", "absolute-first"], + "comma-dangle": ["error", { + "arrays": "always-multiline", + "objects": "always-multiline", + "imports": "always-multiline", + "exports": "always-multiline", + "functions": "never" + }], + "indent": ["error", 2], + "no-console": ["warn", { "allow": ["warn", "error"] }], + "no-undef": 0, + "no-unused-vars": 0, + "no-unused-expressions": "off", + "no-multiple-empty-lines": ["error", { "max": 1, "maxBOF": 2, "maxEOF": 0 }], + "@typescript-eslint/member-ordering": "error", + "@typescript-eslint/explicit-member-accessibility": "error", + "@typescript-eslint/explicit-function-return-type": "error", + "consistent-return": "error", + "@typescript-eslint/no-explicit-any": ["error"], + "@typescript-eslint/no-unused-expressions": ["error"], + "@typescript-eslint/indent": "off", + "@typescript-eslint/naming-convention": [ + "error", + { + "selector": "enum", + "format": ["PascalCase"] + }, + { + "selector": "enumMember", + "format": ["PascalCase"] + } + ] + } +} diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..dfe0770 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1fcc1ef --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +.DS_Store +node_modules +dist +lib +.idea +yarn.lock diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..fce9e5b --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Centre for Science and Technology Studies (CWTS), Leiden University + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..eeccb0c --- /dev/null +++ b/README.md @@ -0,0 +1,199 @@ +# networkanalysis-ts + +This package is a TypeScript port of the [networkanalysis](https://github.com/CWTSLeiden/networkanalysis) package written in Java. The package provides algorithms and data structures for network analysis. Currently, the package focuses on clustering (or community detection) and layout (or mapping) of networks. In particular, the package contains an implementation of the [Leiden algorithm](https://doi.org/10.1038/s41598-019-41695-z) and the [Louvain algorithm](https://doi.org/10.1088/1742-5468/2008/10/P10008) for network clustering and the [VOS technique](https://doi.org/10.1002/asi.21421) for network layout. Only undirected networks are supported. + +The networkanalysis-ts package was developed by [Nees Jan van Eck](https://orcid.org/0000-0001-8448-4521) at the [Centre for Science and Technology Studies (CWTS)](https://www.cwts.nl) at [Leiden University](https://www.universiteitleiden.nl/en) and benefited from contributions by [Olya Stukova](https://github.com/Stukova) and [Nikita Rokotyan](https://github.com/Rokotyan) from [Interacta](https://interacta.io). The networkanalysis package written in Java on which networkanalysis-ts is based was developed by [Vincent Traag](https://orcid.org/0000-0003-3170-3879), [Nees Jan van Eck](https://orcid.org/0000-0001-8448-4521), and [Ludo Waltman](https://orcid.org/0000-0001-8249-1752). + +## Documentation + +Documentation of the source code of networkanalysis-ts is provided in the code in `TSDoc` format. The documentation is also available in a [compiled format](https://neesjanvaneck.github.io/networkanalysis-ts). + +## Installation + +```sh +npm install networkanalysis-ts +``` + +## Usage + +The following code snippet demonstrates how the core classes in networkanalysis-ts can be used to create a network and to perform network normalization, clustering, and layout: + +```typescript +import { Clustering, GradientDescentVOSLayoutAlgorithm, Layout, LeidenAlgorithm, Network } from 'networkanalysis-ts' + +const nRandomStarts = 10 + +// Construct network. +const nNodes = 6 +const edges = [[0, 1, 2, 2, 3, 5, 4], [1, 2, 0, 3, 5, 4, 3]] +const network = new Network({ + nNodes: nNodes, + setNodeWeightsToTotalEdgeWeights: true, + edges: edges, + sortedEdges: false, + checkIntegrity: true, +}) + +// Perform network normalization. +const normalizedNetwork = network.createNormalizedNetworkUsingAssociationStrength() + +// Perform clustering. +let bestClustering: Clustering | undefined +let maxQuality = Number.NEGATIVE_INFINITY +const clusteringAlgorithm = new LeidenAlgorithm() +clusteringAlgorithm.setResolution(0.2) +clusteringAlgorithm.setNIterations(50) +for (let i = 0; i < nRandomStarts; i++) { + const clustering = new Clustering({ nNodes: normalizedNetwork.getNNodes() }) + clusteringAlgorithm.improveClustering(normalizedNetwork, clustering) + const quality = clusteringAlgorithm.calcQuality(normalizedNetwork, clustering) + if (quality > maxQuality) { + bestClustering = clustering + maxQuality = quality + } +} +bestClustering?.orderClustersByNNodes() + +// Perform layout. +let bestLayout: Layout | undefined +let minQuality = Number.POSITIVE_INFINITY +const layoutAlgorithm = new GradientDescentVOSLayoutAlgorithm(); +layoutAlgorithm.setAttraction(2) +layoutAlgorithm.setRepulsion(1) +for (let i = 0; i < nRandomStarts; i++) { + const layout = new Layout({ nNodes: normalizedNetwork.getNNodes() }) + layoutAlgorithm.improveLayout(normalizedNetwork, layout) + const quality = layoutAlgorithm.calcQuality(normalizedNetwork, layout) + if (quality < minQuality) { + bestLayout = layout + minQuality = quality + } +} +bestLayout?.standardize(true) +``` + +The package also includes a `run` module that provides helper classes for running the network analysis algorithms in an easier way. The following code snippet demonstrates the use of the helper classes for constructing a network and for performing network clustering and layout: + +```typescript +import { Node, Link, NetworkClustering, NetworkLayout } from 'networkanalysis-ts/run' + +// Construct network. +const nodes: Node[] = [ + { id: 'James' }, + { id: 'Mary' }, + { id: 'John' }, + { id: 'Linda' }, + { id: 'David' }, + { id: 'Karen' }, +] +const links: Link[] = [ + { node1: nodes[0], node2: nodes[1] }, + { node1: nodes[1], node2: nodes[2] }, + { node1: nodes[2], node2: nodes[0] }, + { node1: nodes[2], node2: nodes[3] }, + { node1: nodes[3], node2: nodes[5] }, + { node1: nodes[5], node2: nodes[4] }, + { node1: nodes[4], node2: nodes[3] }, +] + +// Perform clustering. +new NetworkClustering() + .data(nodes, links) + .qualityFunction('CPM') + .normalization('AssociationStrength') + .resolution(0.2) + .minClusterSize(1) + .algorithm('Leiden') + .randomStarts(10) + .iterations(50) + .randomness(0.01) + .seed(0) + .run() + +// Perform layout. +new NetworkLayout() + .data(nodes, links) + .qualityFunction('VOS') + .normalization('AssociationStrength') + .attraction(2) + .repulsion(1) + .randomStarts(10) + .seed(0) + .run() +``` + +## Demo app + +The GitHub repository of networkanalys-ts also provides a [Svelt](https://svelte.dev) demo app that uses the helper classes discussed above. The source code of the demo app is available in the `app/` folder. The following screenshot shows the output of the demo app when applying it to a journal co-citation network: + +networkanalysis-ts demo app + +## License + +The networkanalysis-ts package is distributed under the [MIT license](LICENSE). + +## Issues + +If you encounter any issues, please report them using the [issue tracker](https://github.com/neesjanvaneck/networkanalysis-ts/issues) on GitHub. + +## Contribution + +You are welcome to contribute to the development of networkanalysis-ts. Please follow the typical GitHub workflow: Fork from this repository and make a pull request to submit your changes. Make sure that your pull request has a clear description and that the code has been properly tested. + +## Development and deployment + +The latest stable version of the code is available from the [`main`](https://github.com/neesjanvaneck/networkanalysis-ts/tree/main) branch on GitHub. The most recent code, which may be under development, is available from the [`develop`](https://github.com/neesjanvaneck/networkanalysis-ts/tree/develop) branch. + +### Requirements + +To run networkanalysis-ts locally and to build production-ready bundles, [Node.js](https://nodejs.org) and [npm](https://www.npmjs.com) need to be installed on your system. + +### Setup + +Run +```sh +npm install +``` +to install all required Node.js packages. + +### Development + +Run +```sh +npm run dev +``` +to build a development version of the demo app and serve it with hot reload at [http://localhost:6800](http://localhost:6800). + +### Deployment + +Run +```sh +npm run build:lib +``` +to build a deployment version of the package. The output is stored in the `lib/` folder. + +Run +```sh +npm run build:app +``` +to build a deployment version of the demo app. The output is stored in the `dist/` folder. + +Run +```sh +npm run build +``` +to build a deployment version of both the package and the demo app. + +### References + +> Traag, V.A., Waltman, L., & Van Eck, N.J. (2019). From Louvain to Leiden: Guaranteeing well-connected communities. *Scientific Reports*, 9, 5233. https://doi.org/10.1038/s41598-019-41695-z + +> Van Eck, N.J., Waltman, L., Dekker, R., & Van den Berg, J. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. *Journal of the American Society for Information Science and Technology*, 61(12), 2405-2416. https://doi.org/10.1002/asi.21421 + +> Waltman, L., Van Eck, N.J., & Noyons, E.C.M. (2010). A unified approach to mapping and clustering of bibliometric networks. *Journal of Informetrics*, 4(4), 629-635. https://doi.org/10.1016/j.joi.2010.07.002 + +> Van Eck, N.J., & Waltman, L. (2009). How to normalize co-occurrence data? An analysis of some well-known similarity measures. *Journal of the American Society for Information Science and Technology*, 60(8), 1635-1651. https://doi.org/10.1002/asi.21075 + +> Blondel, V.D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. *Journal of Statistical Mechanics: Theory and Experiment*, 10, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 + +> Newman, M.E.J. & Girvan, M. (2004). Finding and evaluating community structure in networks. *Physical Review E*, 69(2), 026113, https://doi.org/10.1103/PhysRevE.69.026113. diff --git a/app/App.svelte b/app/App.svelte new file mode 100644 index 0000000..54ecf9f --- /dev/null +++ b/app/App.svelte @@ -0,0 +1,40 @@ + + +
+ + +
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diff --git a/app/data/karate_club_network.txt b/app/data/karate_club_network.txt new file mode 100644 index 0000000..1ee3b2c --- /dev/null +++ b/app/data/karate_club_network.txt @@ -0,0 +1,156 @@ +1 2 +1 3 +1 4 +1 5 +1 6 +1 7 +1 8 +1 9 +1 11 +1 12 +1 13 +1 14 +1 18 +1 20 +1 22 +1 32 +2 1 +2 3 +2 4 +2 8 +2 14 +2 18 +2 20 +2 22 +2 31 +3 1 +3 2 +3 4 +3 8 +3 9 +3 10 +3 14 +3 28 +3 29 +3 33 +4 1 +4 2 +4 3 +4 8 +4 13 +4 14 +5 1 +5 7 +5 11 +6 1 +6 7 +6 11 +6 17 +7 1 +7 5 +7 6 +7 17 +8 1 +8 2 +8 3 +8 4 +9 1 +9 3 +9 31 +9 33 +9 34 +10 3 +10 34 +11 1 +11 5 +11 6 +12 1 +13 1 +13 4 +14 1 +14 2 +14 3 +14 4 +14 34 +15 33 +15 34 +16 33 +16 34 +17 6 +17 7 +18 1 +18 2 +19 33 +19 34 +20 1 +20 2 +20 34 +21 33 +21 34 +22 1 +22 2 +23 33 +23 34 +24 26 +24 28 +24 30 +24 33 +24 34 +25 26 +25 28 +25 32 +26 24 +26 25 +26 32 +27 30 +27 34 +28 3 +28 24 +28 25 +28 34 +29 3 +29 32 +29 34 +30 24 +30 27 +30 33 +30 34 +31 2 +31 9 +31 33 +31 34 +32 1 +32 25 +32 26 +32 29 +32 33 +32 34 +33 3 +33 9 +33 15 +33 16 +33 19 +33 21 +33 23 +33 24 +33 30 +33 31 +33 32 +33 34 +34 9 +34 10 +34 14 +34 15 +34 16 +34 19 +34 20 +34 21 +34 23 +34 24 +34 27 +34 28 +34 29 +34 30 +34 31 +34 32 +34 33 diff --git a/app/declaration.d.ts b/app/declaration.d.ts new file mode 100644 index 0000000..dc4dc46 --- /dev/null +++ b/app/declaration.d.ts @@ -0,0 +1 @@ +declare module '*.txt'; diff --git a/app/helper.ts b/app/helper.ts new file mode 100644 index 0000000..7644eae --- /dev/null +++ b/app/helper.ts @@ -0,0 +1,6 @@ +export const margin = { + left: 10, + top: 10, + right: 10, + bottom: 10, +} diff --git a/app/index.html b/app/index.html new file mode 100644 index 0000000..107e422 --- /dev/null +++ b/app/index.html @@ -0,0 +1,13 @@ + + + + + + + networkanalysis-ts demo app + + + + + + \ No newline at end of file diff --git a/app/index.js b/app/index.js new file mode 100644 index 0000000..54a3cfc --- /dev/null +++ b/app/index.js @@ -0,0 +1,5 @@ +import App from '../app/App.svelte' + +const app = new App({ + target: document.body, +}) diff --git a/app/parseData.ts b/app/parseData.ts new file mode 100644 index 0000000..8b150ee --- /dev/null +++ b/app/parseData.ts @@ -0,0 +1,10 @@ +import { parse } from 'papaparse' +// import data from './data/karate_club_network.txt' +import data from './data/journal_cocitation_network.txt' + +export function parseData (callback: (result: unknown) => void): void { + parse(data, { + skipEmptyLines: true, + complete: result => callback(result), + }) +} diff --git a/app/processData.ts b/app/processData.ts new file mode 100644 index 0000000..161a0d5 --- /dev/null +++ b/app/processData.ts @@ -0,0 +1,19 @@ +import type { Node, Link } from 'networkanalysis-ts/run' + +export function processData (data: string[][]): { nodes: Node[], links: Link[] } { + const nodeById = data.reduce<{ [key: string]: Node }>((acc, curr) => { + if (!acc[curr[0]]) acc[curr[0]] = { id: curr[0] } + if (!acc[curr[1]]) acc[curr[1]] = { id: curr[1] } + return acc + }, {}) + const nodes = Object.values(nodeById) + const links = data.map(link => { + return { + node1: nodeById[link[0]], + node2: nodeById[link[1]], + weight: link[2] !== undefined ? +link[2] : undefined, + } + }) + + return { nodes, links } +} diff --git a/app/renderNetwork.ts b/app/renderNetwork.ts new file mode 100644 index 0000000..d233bcf --- /dev/null +++ b/app/renderNetwork.ts @@ -0,0 +1,82 @@ +import { select } from 'd3-selection' +import { scaleLinear, scaleOrdinal } from 'd3-scale' +import { extent } from 'd3-array' +import { schemeSet3 } from 'd3-scale-chromatic' +import { margin } from './helper' + +const positionXScale = scaleLinear() +const positionYScale = scaleLinear() +const colorScale = scaleOrdinal().range(schemeSet3) + +interface Node { + id: number | string; + x: number; + y: number; + cluster: number; + +} + +interface Link { + node1: Node; + node2: Node; +} + +export function updateScales (width: number, height: number, nodes: Node[]): void { + positionXScale + .range([margin.left, width - margin.left - margin.right]) + .domain(extent(nodes.map(d => d.x)) as number[]) + positionYScale + .range([height - margin.top - margin.bottom, margin.top]) + .domain(extent(nodes.map(d => d.y)) as number[]) + + colorScale.domain(nodes.map(d => d.cluster)) +} + +function linkArc (sourceX: number, sourceY: number, targetX: number, targetY: number): string { + // const r = Math.hypot(targetX - sourceX, targetY - sourceY) + const r = 0 + return ` + M${sourceX},${sourceY} + A${r},${r} 0 0,1 ${targetX},${targetY} + ` +} + +export function renderLinks (svgElement: SVGAElement, links: Link[]): void { + select(svgElement) + .selectAll('line') + .data(links.filter(l => l.node1 && l.node2)) + .enter() + .append('path') + .attr('d', d => linkArc( + positionXScale(d.node1?.x), + positionYScale(d.node1?.y), + positionXScale(d.node2?.x), + positionYScale(d.node2?.y) + )) + .style('fill', 'none') + .style('stroke', 'lightgrey') +} + +export function renderNodes (svgElement: SVGAElement, nodes: Node[]): void { + select(svgElement) + .selectAll('circle') + .data(nodes, (d: Node) => d.id) + .enter() + .append('circle') + .attr('r', 10) + .attr('cx', d => positionXScale(d.x)) + .attr('cy', d => positionYScale(d.y)) + .style('fill', d => colorScale(d.cluster)) + select(svgElement) + .selectAll('text') + .data(nodes) + .enter() + .append('text') + .attr('x', d => positionXScale(d.x)) + .attr('y', d => positionYScale(d.y) + 5) + .attr('font-size', '12px') + .attr('text-anchor', 'middle') + .attr('fill', '#000') + .attr('opacity', 1) + .text(d => d.id) +} diff --git a/app/runAlgorithms.ts b/app/runAlgorithms.ts new file mode 100644 index 0000000..9f71940 --- /dev/null +++ b/app/runAlgorithms.ts @@ -0,0 +1,29 @@ +import { NetworkClustering, NetworkLayout } from 'networkanalysis-ts/run' +import type { Node, Link } from 'networkanalysis-ts/run' + +export function runNetworkClustering (nodes: Node[], links: Link[]): void { + new NetworkClustering() + .data(nodes, links) + .qualityFunction('CPM') + .normalization('AssociationStrength') + .resolution(1) + .minClusterSize(1) + .algorithm('Leiden') + .randomStarts(10) + .iterations(50) + .randomness(0.01) + .seed(0) + .run() +} + +export function runNetworkLayout (nodes: Node[], links: Link[]): void { + new NetworkLayout() + .data(nodes, links) + .qualityFunction('VOS') + .normalization('AssociationStrength') + .attraction(2) + .repulsion(1) + .randomStarts(10) + .seed(0) + .run() +} diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..26fcafb --- /dev/null +++ b/package-lock.json @@ -0,0 +1,13277 @@ +{ + "name": "networkanalysis-ts", + "version": "1.0.0", + "lockfileVersion": 2, + "requires": true, + "packages": { + "": { + "name": "networkanalysis-ts", + "version": "1.0.0", + "license": "MIT", + "dependencies": { + "java-random": "^0.4.0" + }, + "devDependencies": { + "@rollup/plugin-alias": "^3.1.9", + "@rollup/plugin-commonjs": "^16.0.0", + 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network analysis.", + "author": "Nees Jan van Eck (https://github.com/neesjanvaneck)", + "keywords": [ + "network analysis", + "clustering", + "community detection", + "clustering algorithm", + "Leiden algorithm", + "Louvain algorithm", + "layout", + "mapping", + "layout algorithm", + "VOS technique", + "typescript" + ], + "license": "MIT", + "homepage": "https://github.com/neesjanvaneck/networkanalysis-ts#readme", + "repository": { + "type": "git", + "url": "https://github.com/neesjanvaneck/networkanalysis-ts.git" + }, + "bugs": { + "url": "https://github.com/neesjanvaneck/networkanalysis-ts/issues" + }, + "main": "./index.js", + "typings": "./index.d.ts", + "scripts": { + "dev": "cross-env NODE_ENV=development rollup --config rollup.config.app.js --watch", + "build:app": "rm -rf dist && cross-env NODE_ENV=production rollup --config rollup.config.app.js", + "build:lib": "rm -rf lib && rollup --config rollup.config.lib.js && cp ./{package.json,./LICENSE,./README.md} ./lib", + "build": "npm run build:lib && npm run build:app", + "docs": "npx typedoc --options typedoc.json", + "lint": "eslint ./src ./app --ext .ts,.svelte --ignore-path .gitignore", + "publish:lib": "cd ./lib && npm publish && rm -rf .cache" + }, + "dependencies": { + "java-random": "^0.4.0" + }, + "devDependencies": { + "@rollup/plugin-alias": "^3.1.9", + "@rollup/plugin-commonjs": "^16.0.0", + "@rollup/plugin-node-resolve": "^10.0.0", + "@types/d3": "^7.4.0", + "@types/papaparse": "^5.3.4", + "@typescript-eslint/eslint-plugin": "^4.7.0", + "@typescript-eslint/parser": "^4.7.0", + "cross-env": "^7.0.3", + "d3": "^6.7.0", + "eslint": "^7.13.0", + "eslint-config-standard": "^16.0.1", + "eslint-plugin-import": "^2.22.1", + "eslint-plugin-node": "^11.1.0", + "eslint-plugin-promise": "^4.2.1", + "eslint-plugin-standard": "^4.1.0", + "eslint-plugin-svelte3": "^2.7.3", + "husky": "^4.3.6", + "lint-staged": "^10.5.3", + "papaparse": "^5.3.0", + "rollup": "^2.33.1", + "rollup-plugin-html2": "^2.0.0", + "rollup-plugin-livereload": "^2.0.5", + "rollup-plugin-node-builtins": "^2.1.2", + "rollup-plugin-node-globals": "^1.4.0", + "rollup-plugin-postcss": "^4.0.2", + "rollup-plugin-serve": "^1.1.0", + "rollup-plugin-string": "^3.0.0", + "rollup-plugin-svelte": "^6.1.1", + "rollup-plugin-terser": "^7.0.2", + "rollup-plugin-typescript2": "^0.33.0", + "svelte": "^3.49.0", + "svelte-preprocess": "^4.10.7", + "tslib": "^2.4.0", + "typedoc": "^0.22.18", + "typescript": "^4.7.4" + }, + "lint-staged": { + "**/*.{ts,svelte}": "eslint --fix" + }, + "husky": { + "hooks": { + "pre-commit": "lint-staged" + } + } +} diff --git a/rollup.config.app.js b/rollup.config.app.js new file mode 100644 index 0000000..6d3ab15 --- /dev/null +++ b/rollup.config.app.js @@ -0,0 +1,64 @@ +import typescript from 'rollup-plugin-typescript2' +import typescriptCompiler from 'typescript' +import commonjs from '@rollup/plugin-commonjs' +import resolve from '@rollup/plugin-node-resolve' +import html2 from 'rollup-plugin-html2' +import svelte from 'rollup-plugin-svelte' +import serve from 'rollup-plugin-serve' +import postcss from 'rollup-plugin-postcss' +import livereload from 'rollup-plugin-livereload' +import sveltePreprocessor from 'svelte-preprocess' +import { terser } from 'rollup-plugin-terser' +import { string } from 'rollup-plugin-string' +import builtins from 'rollup-plugin-node-builtins' +import globals from 'rollup-plugin-node-globals' + +const isDevelopment = process.env.NODE_ENV === 'development' +const plugins = [ + svelte({ + dev: isDevelopment, + extensions: ['.svelte'], + preprocess: sveltePreprocessor(), + emitCss: true, + }), + globals(), + builtins(), + typescript({ + tsconfig: './tsconfig.app.json', + typescript: typescriptCompiler, + }), + resolve(), + postcss({ + extract: true, + }), + string({ + include: '**/*.txt', + }), + commonjs({ include: 'node_modules/**', extensions: ['.js', '.ts'] }), + html2({ + template: 'app/index.html', + }), +] + +if (isDevelopment) { + plugins.push( + serve({ + contentBase: './dist', + open: false, + port: 6800, + }), + livereload({ watch: './dist' }) + ) +} else { + plugins.push(terser()) +} + +module.exports = { + input: 'app/index.js', + output: { + file: 'dist/index.js', + sourcemap: true, + format: 'iife', + }, + plugins, +} diff --git a/rollup.config.lib.js b/rollup.config.lib.js new file mode 100644 index 0000000..f1452c1 --- /dev/null +++ b/rollup.config.lib.js @@ -0,0 +1,38 @@ +import typescript from 'rollup-plugin-typescript2' +import typescriptCompiler from 'typescript' +import commonjs from '@rollup/plugin-commonjs' +import resolve from '@rollup/plugin-node-resolve' +import { terser } from 'rollup-plugin-terser' +import pkg from './package.json' + +const externals = [ + ...Object.keys(pkg.dependencies || {}), + ...Object.keys(pkg.peerDependencies || {}), +] + +const regexesOfPackages = externals // To prevent having node_modules in the build files + .map(packageName => new RegExp(`^${packageName}(/.*)?`)) + +const plugins = [ + typescript({ + tsconfig: './tsconfig.lib.json', + typescript: typescriptCompiler, + }), + resolve(), + commonjs({ include: 'node_modules/**', extensions: ['.js', '.ts'] }), + terser(), +] + +module.exports = { + input: ['src/index.ts', 'src/run/index.ts', 'src/utils/index.ts'], + external: regexesOfPackages, + output: [ + { + dir: 'lib', + format: 'esm', + preserveModules: true, + preserveModulesRoot: './src', + }, + ], + plugins, +} diff --git a/src/CPMClusteringAlgorithm.ts b/src/CPMClusteringAlgorithm.ts new file mode 100644 index 0000000..b161783 --- /dev/null +++ b/src/CPMClusteringAlgorithm.ts @@ -0,0 +1,257 @@ +import Clustering, { ClusteringParametersWithNNodes } from './clustering' +import Network from './network' +import QualityClusteringAlgorithm from './qualityClusteringAlgorithm' +import { calcMaximum } from './utils/arrays' + +/** + * Abstract base class for clustering algorithms that use the CPM quality + * function. + */ +export default abstract class CPMClusteringAlgorithm implements QualityClusteringAlgorithm { + /** + * Default resolution parameter. + */ + public static readonly DEFAULT_RESOLUTION: number = 1 + + /** + * Resolution parameter. + */ + protected resolution!: number + + /** + * Constructs a CPM clustering algorithm. + */ + public constructor () { + this.initializeBasedOnResolution(CPMClusteringAlgorithm.DEFAULT_RESOLUTION) + } + + /** + * Initializes a CPM clustering algorithm with a specified resolution + * parameter. + * + * @param resolution Resolution parameter + */ + public initializeBasedOnResolution (resolution: number): void { + this.resolution = resolution + } + + /** + * Returns the resolution parameter. + * + * @return Resolution parameter + */ + public getResolution (): number { + return this.resolution + } + + /** + * Sets the resolution parameter. + * + * @param resolution Resolution parameter + */ + public setResolution (resolution: number): void { + this.resolution = resolution + } + + /** + * Calculates the quality of a clustering using the CPM quality function. + * + * The CPM quality function is given by + * + * ``` + * 1 / (2 * m) * sum(d(c[i], c[j]) * (a[i][j] - resolution * n[i] * + * n[j])), + * ``` + * + * where `a[i][j]` is the weight of the edge between nodes `i` and `j`, + * `n[i]` is the weight of node `i`, `m` is the total edge weight, and + * `resolution` is the resolutionparameter. The function `d(c[i], c[j])` + * equals 1 if nodes `i` and `j` belong to the same cluster and 0 otherwise. + * The sum is taken over all pairs of nodes `i` and `j`. + * + * Modularity can be expressed in terms of CPM by setting `n[i]` equal to + * the total weight of the edges between node `i` and its neighbors and by + * rescaling the resolution parameter by `2 * m`. + * + * @param network Network + * @param clustering Clustering + * + * @return Quality of the clustering + */ + public calcQuality (network: Network, clustering: Clustering): number { + let quality = 0 + + for (let i = 0; i < network.nNodes; i++) { + const j = clustering.clusters[i] + for (let k = network.firstNeighborIndices[i]; k < network.firstNeighborIndices[i + 1]; k++) { + if (clustering.clusters[network.neighbors[k]] === j) { + quality += network.edgeWeights[k] + } + } + } + quality += network.totalEdgeWeightSelfLinks + + const clusterWeights = new Array(clustering.nClusters).fill(0) + for (let i = 0; i < network.nNodes; i++) { + clusterWeights[clustering.clusters[i]] += network.nodeWeights[i] + } + for (let i = 0; i < clustering.nClusters; i++) { + quality -= clusterWeights[i] * clusterWeights[i] * this.resolution + } + + quality /= 2 * network.getTotalEdgeWeight() + network.totalEdgeWeightSelfLinks + + return quality + } + + /** + * Removes a cluster from a clustering by merging the cluster with another + * cluster. If a cluster has no connections with other clusters, it cannot + * be removed. + * + * @param network Network + * @param clustering Clustering + * @param cluster Cluster to be removed + * + * @return Cluster with which the cluster to be removed has been merged, or + * -1 if the cluster could not be removed + */ + public removeCluster (network: Network, clustering: Clustering, cluster: number): number { + const clusterWeights = new Array(clustering.nClusters).fill(0) + const totalEdgeWeightPerCluster = new Array(clustering.nClusters).fill(0) + for (let i = 0; i < network.nNodes; i++) { + clusterWeights[clustering.clusters[i]] += network.nodeWeights[i] + if (clustering.clusters[i] === cluster) { + for (let j = network.firstNeighborIndices[i]; j < network.firstNeighborIndices[i + 1]; j++) { + totalEdgeWeightPerCluster[clustering.clusters[network.neighbors[j]]] += network.edgeWeights[j] + } + } + } + + let i = -1 + let maxQualityFunction = 0 + for (let j = 0; j < clustering.nClusters; j++) { + if ((j !== cluster) && (clusterWeights[j] > 0)) { + const qualityFunction = totalEdgeWeightPerCluster[j] / clusterWeights[j] + if (qualityFunction > maxQualityFunction) { + i = j + maxQualityFunction = qualityFunction + } + } + } + + if (i >= 0) { + for (let j = 0; j < network.nNodes; j++) { + if (clustering.clusters[j] === cluster) { + clustering.clusters[j] = i + } + } + if (cluster === clustering.nClusters - 1) { + clustering.nClusters = calcMaximum(clustering.clusters) + 1 + } + } + + return i + } + + /** + * Removes small clusters from a clustering. Clusters are merged until each + * cluster contains at least a certain minimum number of nodes. + * + * @param network Network + * @param clustering Clustering + * @param minNNodesPerCluster Minimum number of nodes per cluster + * + * @return Boolean indicating whether any clusters have been removed + */ + public removeSmallClustersBasedOnNNodes (network: Network, clustering: Clustering, minNNodesPerCluster: number): boolean { + const reducedNetwork = network.createReducedNetwork(clustering) + const clusteringReducedNetwork = new Clustering({ nNodes: reducedNetwork.nNodes } as ClusteringParametersWithNNodes) + + const nNodesPerCluster = clustering.getNNodesPerCluster() + + let i: number + do { + i = -1 + let nNodesSmallestCluster = minNNodesPerCluster + for (let j = 0; j < clustering.nClusters; j++) { + if ((nNodesPerCluster[j] > 0) && (nNodesPerCluster[j] < nNodesSmallestCluster)) { + i = j + nNodesSmallestCluster = nNodesPerCluster[j] + } + } + + if (i >= 0) { + const j = this.removeCluster(reducedNetwork, clusteringReducedNetwork, i) + if (j >= 0) { + nNodesPerCluster[j] += nNodesPerCluster[i] + } + nNodesPerCluster[i] = 0 + } + } while (i >= 0) + + clustering.mergeClusters(clusteringReducedNetwork) + + return clusteringReducedNetwork.nClusters < reducedNetwork.nNodes + } + + /** + * Removes small clusters from a clustering. Clusters are merged until each + * cluster has at least a certain minimum total node weight. + * + * The total node weight of a cluster equals the sum of the weights of the + * nodes belonging to the cluster. + * + * @param network Network + * @param clustering Clustering + * @param minClusterWeight Minimum total node weight of a cluster + * + * @return Boolean indicating whether any clusters have been removed + */ + public removeSmallClustersBasedOnWeight (network: Network, clustering: Clustering, minClusterWeight: number): boolean { + const reducedNetwork = network.createReducedNetwork(clustering) + const clusteringReducedNetwork = new Clustering({ nNodes: reducedNetwork.nNodes } as ClusteringParametersWithNNodes) + + const clusterWeights = reducedNetwork.nodeWeights.slice() + + let i: number + do { + i = -1 + let weightSmallestCluster = minClusterWeight + for (let j = 0; j < clusteringReducedNetwork.nClusters; j++) { + if ((clusterWeights[j] > 0) && (clusterWeights[j] < weightSmallestCluster)) { + i = j + weightSmallestCluster = clusterWeights[j] + } + } + + if (i >= 0) { + const j = this.removeCluster(reducedNetwork, clusteringReducedNetwork, i) + if (j >= 0) { + clusterWeights[j] += clusterWeights[i] + } + clusterWeights[i] = 0 + } + } while (i >= 0) + + clustering.mergeClusters(clusteringReducedNetwork) + + return clusteringReducedNetwork.nClusters < reducedNetwork.nNodes + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public abstract clone (): CPMClusteringAlgorithm + + /** + * Finds a clustering of the nodes in a network. + * + * @param network Network + * + * @return Clustering + */ + public abstract findClustering (network: Network): Clustering +} diff --git a/src/README.md b/src/README.md new file mode 100644 index 0000000..a0db0cc --- /dev/null +++ b/src/README.md @@ -0,0 +1,5 @@ +# networkanalysis-ts + +This package is a TypeScript port of the [networkanalysis](https://github.com/CWTSLeiden/networkanalysis) package written in Java. The package provides algorithms and data structures for network analysis. Currently, the package focuses on clustering (or community detection) and layout (or mapping) of networks. In particular, the package contains an implementation of the [Leiden algorithm](https://doi.org/10.1038/s41598-019-41695-z) and the [Louvain algorithm](https://doi.org/10.1088/1742-5468/2008/10/P10008) for network clustering and the [VOS technique](https://doi.org/10.1002/asi.21421) for network layout. Only undirected networks are supported. + +The networkanalysis-ts package was developed by [Nees Jan van Eck](https://orcid.org/0000-0001-8448-4521) at the [Centre for Science and Technology Studies (CWTS)](https://www.cwts.nl) at [Leiden University](https://www.universiteitleiden.nl/en) and benefited from contributions by [Olya Stukova](https://github.com/Stukova) and [Nikita Rokotyan](https://github.com/Rokotyan) from [Interacta](https://interacta.io). The networkanalysis package written in Java on which networkanalysis-ts is based was developed by [Vincent Traag](https://orcid.org/0000-0003-3170-3879), [Nees Jan van Eck](https://orcid.org/0000-0001-8448-4521), and [Ludo Waltman](https://orcid.org/0000-0001-8249-1752). diff --git a/src/VOSLayoutAlgorithm.ts b/src/VOSLayoutAlgorithm.ts new file mode 100644 index 0000000..27c3105 --- /dev/null +++ b/src/VOSLayoutAlgorithm.ts @@ -0,0 +1,197 @@ +import Layout from './layout' +import Network from './network' +import QualityLayoutAlgorithm from './qualityLayoutAlgorithm' +import { fastPow } from './utils/fastMath' + +/** + * Abstract base class for layout algorithms that use the VOS quality function. + */ +export default abstract class VOSLayoutAlgorithm implements QualityLayoutAlgorithm { + /** + * Default attraction parameter. + */ + public static readonly DEFAULT_ATTRACTION: number = 2 + + /** + * Default repulsion parameter. + */ + public static readonly DEFAULT_REPULSION: number = 1 + + /** + * Default edge weight increment parameter. + */ + public static readonly DEFAULT_EDGE_WEIGHT_INCREMENT: number = 0 + + /** + * Attraction parameter. + */ + protected attraction!: number + + /** + * Repulsion parameter. + */ + protected repulsion!: number + + /** + * Edge weight increment parameter. + */ + protected edgeWeightIncrement!: number + + /** + * Constructs a VOS layout algorithm. + */ + public constructor () { + this.initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement(VOSLayoutAlgorithm.DEFAULT_ATTRACTION, VOSLayoutAlgorithm.DEFAULT_REPULSION, VOSLayoutAlgorithm.DEFAULT_EDGE_WEIGHT_INCREMENT) + } + + /** + * Initializes a VOS layout algorithm with a specified attraction parameter, + * repulsion parameter, and edge weight increment parameter. + * + * @param attraction Attraction parameter + * @param repulsion Repulsion parameter + * @param edgeWeightIncrement Edge weight increment parameter + */ + public initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement (attraction: number, repulsion: number, edgeWeightIncrement: number): void { + this.attraction = attraction + this.repulsion = repulsion + this.edgeWeightIncrement = edgeWeightIncrement + } + + /** + * Returns the attraction parameter. + * + * @return Attraction parameter + */ + public getAttraction (): number { + return this.attraction + } + + /** + * Returns the repulsion parameter. + * + * @return Repulsion parameter + */ + public getRepulsion (): number { + return this.repulsion + } + + /** + * Returns the edge weight increment parameter. + * + * @return Edge weight increment parameter + */ + public getEdgeWeightIncrement (): number { + return this.edgeWeightIncrement + } + + /** + * Sets the attraction parameter. + * + * @param attraction Attraction parameter + */ + public setAttraction (attraction: number): void { + this.attraction = attraction + } + + /** + * Sets the repulsion parameter. + * + * @param repulsion Repulsion parameter + */ + public setRepulsion (repulsion: number): void { + this.repulsion = repulsion + } + + /** + * Sets the edge weight increment parameter. + * + * @param edgeWeightIncrement Edge weight increment parameter + */ + public setEdgeWeightIncrement (edgeWeightIncrement: number): void { + this.edgeWeightIncrement = edgeWeightIncrement + } + + /** + * Calculates the quality of a layout using the VOS quality function. + * + * The VOS quality function is given by + * + * ``` + * 1 / attraction * sum(a[i][j] * d(x[i], x[j]) ^ + * attraction) - 1 / repulsion * sum(d(x[i], x[j]) ^ + * repulsion), + * ``` + * + * where `a[i][j]` is the weight of the edge between nodes `i` and `j` and + * `x[i] = (x[i][1], x[i][2])` are the coordinates of node `i`. The function + * `d(x[i], x[j])` is the Euclidean distance between nodes `i` and `j`. The + * sum is taken over all pairs of nodes `i` and `j` with `j < i`. The + * attraction parameter must be greater than the repulsion parameter. The + * lower the value of the VOS quality function, the higher the quality of the + * layout. + * + * @param network Network + * @param layout Layout + * + * @return Quality of the layout + */ + public calcQuality (network: Network, layout: Layout): number { + let quality = 0 + + for (let i = 0; i < network.nNodes; i++) { + for (let j = network.firstNeighborIndices[i]; j < network.firstNeighborIndices[i + 1]; j++) { + if (network.neighbors[j] < i) { + const distance1 = layout.coordinates[0][i] - layout.coordinates[0][network.neighbors[j]] + const distance2 = layout.coordinates[1][i] - layout.coordinates[1][network.neighbors[j]] + const distance = Math.sqrt(distance1 * distance1 + distance2 * distance2) + if (this.attraction !== 0) { + quality += network.edgeWeights[j] * fastPow(distance, this.attraction) / this.attraction + } else { + quality += network.edgeWeights[j] * Math.log(distance) + } + } + } + } + + for (let i = 0; i < network.nNodes; i++) { + for (let j = 0; j < i; j++) { + const distance1 = layout.coordinates[0][i] - layout.coordinates[0][j] + const distance2 = layout.coordinates[1][i] - layout.coordinates[1][j] + const distance = Math.sqrt(distance1 * distance1 + distance2 * distance2) + + if (this.repulsion !== 0) { + quality -= network.nodeWeights[i] * network.nodeWeights[j] * fastPow(distance, this.repulsion) / this.repulsion + } else { + quality -= network.nodeWeights[i] * network.nodeWeights[j] * Math.log(distance) + } + + if (this.edgeWeightIncrement > 0) { + if (this.attraction !== 0) { + quality += this.edgeWeightIncrement * fastPow(distance, this.attraction) / this.attraction + } else { + quality += this.edgeWeightIncrement * Math.log(distance) + } + } + } + } + + return quality + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public abstract clone (): VOSLayoutAlgorithm + + /** + * Finds a layout of the nodes in a network. + * + * @param network Network + * + * @return Layout + */ + public abstract findLayout (network: Network): Layout +} diff --git a/src/clustering.ts b/src/clustering.ts new file mode 100644 index 0000000..94d17e6 --- /dev/null +++ b/src/clustering.ts @@ -0,0 +1,278 @@ +import { calcMaximum } from './utils/arrays' + +/** + * Interface for specifying the number of nodes when constructing a clustering. + */ +export interface ClusteringParametersWithNNodes { + /** + * Number of nodes. + */ + nNodes: number +} + +/** + * Interface for specifying the cluster for each node when constructing a + * clustering. + */ +export interface ClusteringParametersWithClusters { + /** + * Cluster of each node. + */ + clusters: number[] +} + +/** + * Clustering of the nodes in a network. + * + * Each node belongs to exactly one cluster. + */ +export default class Clustering { + /** + * Cluster of each node. + */ + public clusters!: number[] + + /** + * Number of clusters. + */ + public nClusters!: number + + /** + * Number of nodes. + */ + protected nNodes!: number + + /** + * Constructs a clustering. + * + * @param parameters Clustering constructor parameters + */ + public constructor (parameters: ClusteringParametersWithNNodes | ClusteringParametersWithClusters) { + if (typeof (parameters as ClusteringParametersWithNNodes).nNodes !== 'undefined') { + const clusteringParameters = parameters as ClusteringParametersWithNNodes + this.initializeBasedOnNNodes(clusteringParameters.nNodes) + } else if (typeof (parameters as ClusteringParametersWithClusters).clusters !== 'undefined') { + const clusteringParameters = parameters as ClusteringParametersWithClusters + this.initializeBasedOnClusters(clusteringParameters.clusters) + } + } + + /** + * Initializes a singleton clustering for a specified number of nodes. + * + * @param nNodes Number of nodes + */ + public initializeBasedOnNNodes (nNodes: number): void { + this.nNodes = nNodes + this.initSingletonClustersHelper() + } + + /** + * Initializes a clustering using a specified cluster for each node. + * + * @param clusters Cluster of each node + */ + public initializeBasedOnClusters (clusters: number[]): void { + this.nNodes = clusters.length + this.clusters = clusters.slice() + this.nClusters = calcMaximum(clusters) + 1 + } + + /** + * Clones the clustering. + * + * @return Cloned clustering + */ + public clone (): Clustering { + const clonedClustering = new Clustering({ nNodes: this.nNodes } as ClusteringParametersWithNNodes) + clonedClustering.nClusters = this.nClusters + clonedClustering.clusters = this.clusters.slice() + return clonedClustering + } + + /** + * Returns the number of nodes. + * + * @return Number of nodes + */ + public getNNodes (): number { + return this.nNodes + } + + /** + * Returns the number of clusters. + * + * @return Number of clusters + */ + public getNClusters (): number { + return this.nClusters + } + + /** + * Returns the cluster of each node. + * + * @return Cluster of each node + */ + public getClusters (): number[] { + return this.clusters.slice() + } + + /** + * Returns the cluster of a node. + * + * @param node Node + * + * @return Cluster + */ + public getCluster (node: number): number { + return this.clusters[node] + } + + /** + * Returns the number of nodes per cluster. + * + * @return Number of nodes per cluster + */ + public getNNodesPerCluster (): number[] { + const nNodesPerCluster = new Array(this.nClusters).fill(0) + for (let i = 0; i < this.nNodes; i++) { + nNodesPerCluster[this.clusters[i]]++ + } + return nNodesPerCluster + } + + /** + * Returns a list of nodes per cluster. + * + * @return List of nodes per cluster + */ + public getNodesPerCluster (): number[][] { + const nodesPerCluster = new Array>(this.nClusters) + const nNodesPerCluster = this.getNNodesPerCluster() + for (let i = 0; i < this.nClusters; i++) { + nodesPerCluster[i] = new Array(nNodesPerCluster[i]).fill(0) + nNodesPerCluster[i] = 0 + } + for (let i = 0; i < this.nNodes; i++) { + nodesPerCluster[this.clusters[i]][nNodesPerCluster[this.clusters[i]]] = i + nNodesPerCluster[this.clusters[i]]++ + } + return nodesPerCluster + } + + /** + * Assigns a node to a cluster. + * + * @param node Node + * @param cluster Cluster + */ + public setCluster (node: number, cluster: number): void { + this.clusters[node] = cluster + this.nClusters = Math.max(this.nClusters, cluster + 1) + } + + /** + * Initializes a singleton clustering. + * + * Each node `i` is assigned to a cluster `i`. + */ + public initSingletonClusters (): void { + this.initSingletonClustersHelper() + } + + /** + * Removes empty clusters. + * + * Clusters are relabeled to follow a strictly consecutive numbering + * `0, ..., nClusters - 1`. + */ + public removeEmptyClusters (): void { + const nonEmptyClusters = new Array(this.nClusters).fill(false) + const newClusters = new Array(this.nClusters).fill(0) + for (let i = 0; i < this.nNodes; i++) { + nonEmptyClusters[this.clusters[i]] = true + } + let i = 0 + for (let j = 0; j < this.nClusters; j++) { + if (nonEmptyClusters[j]) { + newClusters[j] = i + i++ + } + } + this.nClusters = i + for (i = 0; i < this.nNodes; i++) { + this.clusters[i] = newClusters[this.clusters[i]] + } + } + + /** + * Orders the clusters in decreasing order of their number of nodes. + * + * @see {@link orderClustersByWeight} + */ + public orderClustersByNNodes (): void { + this.orderClustersByWeight(new Array(this.nNodes).fill(1)) + } + + /** + * Orders the clusters in decreasing order of their total node weight. + * + * The total node weight of a cluster equals the sum of the weights of the + * nodes belonging to the cluster. + * + * @param nodeWeights Node weights + * + * @see {@link orderClustersByNNodes} + */ + public orderClustersByWeight (nodeWeights: number[]): void { + interface Cluster { + cluster: number + weight: number + } + + const clusterWeights = new Array(this.nClusters).fill(0) + for (let i = 0; i < this.nNodes; i++) { + clusterWeights[this.clusters[i]] += nodeWeights[i] + } + const clusters = new Array(this.nClusters) + for (let i = 0; i < this.nClusters; i++) { + clusters[i] = { + cluster: i, + weight: clusterWeights[i], + } + } + + clusters.sort((a, b) => ((b.weight > a.weight) ? 1 : ((b.weight < a.weight) ? -1 : 0))) + + const newClusters = new Array(this.nClusters).fill(0) + let i = 0 + do { + newClusters[clusters[i].cluster] = i + i++ + } while ((i < this.nClusters) && (clusters[i].weight > 0)) + this.nClusters = i + for (i = 0; i < this.nNodes; i++) { + this.clusters[i] = newClusters[this.clusters[i]] + } + } + + /** + * Merges the clusters based on a clustering of the clusters. + * + * @param clustering Clustering of the clusters + */ + public mergeClusters (clustering: Clustering): void { + for (let i = 0; i < this.nNodes; i++) { + this.clusters[i] = clustering.clusters[this.clusters[i]] + } + this.nClusters = clustering.nClusters + } + + private initSingletonClustersHelper (): void { + this.clusters = new Array(this.nNodes) + for (let i = 0; i < this.nNodes; i++) { + this.clusters[i] = i + } + this.nClusters = this.nNodes + } +} diff --git a/src/clusteringAlgorithm.ts b/src/clusteringAlgorithm.ts new file mode 100644 index 0000000..3e4adbd --- /dev/null +++ b/src/clusteringAlgorithm.ts @@ -0,0 +1,16 @@ +import Clustering from './clustering' +import Network from './network' + +/** + * Interface for clustering algorithms. + */ +export default interface ClusteringAlgorithm { + /** + * Finds a clustering of the nodes in a network. + * + * @param network Network + * + * @return Clustering + */ + findClustering (network: Network): Clustering +} diff --git a/src/componentsAlgorithm.ts b/src/componentsAlgorithm.ts new file mode 100644 index 0000000..74f07e7 --- /dev/null +++ b/src/componentsAlgorithm.ts @@ -0,0 +1,54 @@ +import ClusteringAlgorithm from './clusteringAlgorithm' +import Clustering, { ClusteringParametersWithNNodes } from './clustering' +import Network from './network' + +/** + * Algorithm for finding the connected components of a network. + */ +export default class ComponentsAlgorithm implements ClusteringAlgorithm { + /** + * Constructs a components algorithm. + */ + // public constructor () {} + + /** + * Finds the connected components of a network. + * + * @param network Network + * + * @return Connected components + */ + public findClustering (network: Network): Clustering { + const clustering = new Clustering({ nNodes: network.getNNodes() } as ClusteringParametersWithNNodes) + + clustering.nClusters = 0 + const nodesVisited = new Array(network.nNodes).fill(false) + const nodes = new Array(network.nNodes).fill(0) + for (let i = 0; i < network.nNodes; i++) { + if (!nodesVisited[i]) { + clustering.clusters[i] = clustering.nClusters + nodesVisited[i] = true + nodes[0] = i + let j = 1 + let k = 0 + do { + for (let l = network.firstNeighborIndices[nodes[k]]; l < network.firstNeighborIndices[nodes[k] + 1]; l++) { + if (!nodesVisited[network.neighbors[l]]) { + clustering.clusters[network.neighbors[l]] = clustering.nClusters + nodesVisited[network.neighbors[l]] = true + nodes[j] = network.neighbors[l] + j++ + } + } + k++ + } while (k < j) + + clustering.nClusters++ + } + } + + clustering.orderClustersByNNodes() + + return clustering + } +} diff --git a/src/fastLocalMovingAlgorithm.ts b/src/fastLocalMovingAlgorithm.ts new file mode 100644 index 0000000..5899ad5 --- /dev/null +++ b/src/fastLocalMovingAlgorithm.ts @@ -0,0 +1,232 @@ +import Random from 'java-random' +import IterativeCPMClusteringAlgorithm from './iterativeCPMClusteringAlgorithm' +import Clustering from './clustering' +import Network from './network' +import { generateRandomPermutation } from './utils/arrays' + +/** + * Fast local moving algorithm. + * + * The fast local moving algorithm first adds all nodes in a network to a + * queue. It then removes a node from the queue. The node is moved to the + * cluster that results in the largest increase in the quality function. If the + * current cluster assignment of the node is already optimal, the node is not + * moved. If the node is moved to a different cluster, the neighbors of the + * node that do not belong to the node's new cluster and that are not yet in + * the queue are added to the queue. The algorithm continues removing nodes + * from the queue until the queue is empty. + * + * The fast local moving algorithm provides a fast variant of the {@link + * StandardLocalMovingAlgorithm}. + */ +export default class FastLocalMovingAlgorithm extends IterativeCPMClusteringAlgorithm { + /** + * Random number generator. + */ + protected random!: Random + + /** + * Constructs a fast local moving algorithm. + */ + public constructor () { + super() + this.initializeBasedOnRandom(new Random()) + } + + /** + * Initializes a fast local merging algorithm. + * + * @param random Random number generator + */ + public initializeBasedOnRandom (random: Random): void { + this.initializeBasedOnResolutionAndNIterationsAndRandom(FastLocalMovingAlgorithm.DEFAULT_RESOLUTION, FastLocalMovingAlgorithm.DEFAULT_N_ITERATIONS, random) + } + + /** + * Initializes a fast local merging algorithm for a specified resolution + * parameter and number of iterations. + * + * @param resolution Resolution parameter + * @param nIterations Number of iterations + * @param random Random number generator + */ + public initializeBasedOnResolutionAndNIterationsAndRandom (resolution: number, nIterations: number, random: Random): void { + super.initializeBasedOnResolutionAndNIterations(resolution, nIterations) + this.random = random + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public clone (): FastLocalMovingAlgorithm { + const clonedAlgorithm = new FastLocalMovingAlgorithm() + clonedAlgorithm.resolution = this.resolution + clonedAlgorithm.nIterations = this.nIterations + clonedAlgorithm.random = this.random + return clonedAlgorithm + } + + /** + * Improves a clustering by performing one iteration of the fast local + * moving algorithm. + * + * The fast local moving algorithm first adds all nodes in a network to a + * queue. It then removes a node from the queue. The node is moved to the + * cluster that results in the largest increase in the quality function. If + * the current cluster assignment of the node is already optimal, the node + * is not moved. If the node is moved to a different cluster, the neighbors + * of the node that do not belong to the node's new cluster and that are + * not yet in the queue are added to the queue. The algorithm continues + * removing nodes from the queue until the queue is empty. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + protected improveClusteringOneIteration (network: Network, clustering: Clustering): boolean { + if (network.nNodes === 1) { + return false + } + + let update = false + + const clusterWeights = new Array(network.nNodes).fill(0) + const nNodesPerCluster = new Array(network.nNodes).fill(0) + for (let i = 0; i < network.nNodes; i++) { + clusterWeights[clustering.clusters[i]] += network.nodeWeights[i] + nNodesPerCluster[clustering.clusters[i]]++ + } + + let nUnusedClusters = 0 + const unusedClusters = new Array(network.nNodes - 1).fill(0) + for (let i = network.nNodes - 1; i >= 0; i--) { + if (nNodesPerCluster[i] === 0) { + unusedClusters[nUnusedClusters] = i + nUnusedClusters++ + } + } + + const nodeOrder = generateRandomPermutation(network.nNodes, this.random) + + /* + * Iterate over the nodeOrder array in a cyclical manner. When the end + * of the array has been reached, start again from the beginning. The + * queue of nodes that still need to be visited is given by + * nodeOrder[i], ..., nodeOrder[i + nUnstableNodes - 1]. Continue + * iterating until the queue is empty. + */ + const edgeWeightPerCluster = new Array(network.nNodes).fill(0) + const neighboringClusters = new Array(network.nNodes).fill(0) + const stableNodes = new Array(network.nNodes).fill(false) + let nUnstableNodes = network.nNodes + let i = 0 + do { + const j = nodeOrder[i] + + const currentCluster = clustering.clusters[j] + + // Remove the currently selected node from its current cluster. + clusterWeights[currentCluster] -= network.nodeWeights[j] + nNodesPerCluster[currentCluster]-- + if (nNodesPerCluster[currentCluster] === 0) { + unusedClusters[nUnusedClusters] = currentCluster + nUnusedClusters++ + } + + /* + * Identify the neighboring clusters of the currently selected + * node, that is, the clusters with which the currently selected + * node is connected. An empty cluster is also included in the set + * of neighboring clusters. In this way, it is always possible that + * the currently selected node will be moved to an empty cluster. + */ + neighboringClusters[0] = unusedClusters[nUnusedClusters - 1] + let nNeighboringClusters = 1 + for (let k = network.firstNeighborIndices[j]; k < network.firstNeighborIndices[j + 1]; k++) { + const l = clustering.clusters[network.neighbors[k]] + if (edgeWeightPerCluster[l] === 0) { + neighboringClusters[nNeighboringClusters] = l + nNeighboringClusters++ + } + edgeWeightPerCluster[l] += network.edgeWeights[k] + } + + /* + * For each neighboring cluster of the currently selected node, + * calculate the increment of the quality function obtained by + * moving the currently selected node to the neighboring cluster. + * Determine the neighboring cluster for which the increment of the + * quality function is largest. The currently selected node will be + * moved to this optimal cluster. In order to guarantee convergence + * of the algorithm, if the old cluster of the currently selected + * node is optimal but there are also other optimal clusters, the + * currently selected node will be moved back to its old cluster. + */ + let bestCluster = currentCluster + let maxQualityValueIncrement = edgeWeightPerCluster[currentCluster] - network.nodeWeights[j] * clusterWeights[currentCluster] * this.resolution + for (let k = 0; k < nNeighboringClusters; k++) { + const l = neighboringClusters[k] + + const qualityValueIncrement = edgeWeightPerCluster[l] - network.nodeWeights[j] * clusterWeights[l] * this.resolution + if (qualityValueIncrement > maxQualityValueIncrement) { + bestCluster = l + maxQualityValueIncrement = qualityValueIncrement + } + + edgeWeightPerCluster[l] = 0 + } + + /* + * Move the currently selected node to its new cluster. Update the + * clustering statistics. + */ + clusterWeights[bestCluster] += network.nodeWeights[j] + nNodesPerCluster[bestCluster]++ + if (bestCluster === unusedClusters[nUnusedClusters - 1]) { + nUnusedClusters-- + } + + /* + * Mark the currently selected node as stable and remove it from + * the queue. + */ + stableNodes[j] = true + nUnstableNodes-- + + /* + * If the new cluster of the currently selected node is different + * from the old cluster, some further updating of the clustering + * statistics is performed. Also, the neighbors of the currently + * selected node that do not belong to the new cluster are marked + * as unstable and are added to the queue. + */ + if (bestCluster !== currentCluster) { + clustering.clusters[j] = bestCluster + if (bestCluster >= clustering.nClusters) { + clustering.nClusters = bestCluster + 1 + } + + for (let k = network.firstNeighborIndices[j]; k < network.firstNeighborIndices[j + 1]; k++) { + if (stableNodes[network.neighbors[k]] && (clustering.clusters[network.neighbors[k]] !== bestCluster)) { + stableNodes[network.neighbors[k]] = false + nUnstableNodes++ + nodeOrder[(i + nUnstableNodes < network.nNodes) ? (i + nUnstableNodes) : (i + nUnstableNodes - network.nNodes)] = network.neighbors[k] + } + } + + update = true + } + + i = (i < network.nNodes - 1) ? (i + 1) : 0 + } while (nUnstableNodes > 0) + + if (update) { + clustering.removeEmptyClusters() + } + + return update + } +} diff --git a/src/gradientDescentVOSLayoutAlgorithm.ts b/src/gradientDescentVOSLayoutAlgorithm.ts new file mode 100644 index 0000000..637b8a3 --- /dev/null +++ b/src/gradientDescentVOSLayoutAlgorithm.ts @@ -0,0 +1,361 @@ +import Random from 'java-random' +import Layout, { LayoutConstructorParametersWithNNodesAndRandom } from './layout' +import Network from './network' +import VOSLayoutAlgorithm from './VOSLayoutAlgorithm' +import { generateRandomPermutation } from './utils/arrays' +import { fastPow } from './utils/fastMath' + +/** + * Gradient descent VOS layout algorithm. + */ +export default class GradientDescentVOSLayoutAlgorithm extends VOSLayoutAlgorithm { + /** + * Default maximum number of iterations. + */ + public static readonly DEFAULT_MAX_N_ITERATIONS: number = 1000 + + /** + * Default initial step size. + */ + public static readonly DEFAULT_INITIAL_STEP_SIZE: number = 1 + + /** + * Default minimum step size. + */ + public static readonly DEFAULT_MIN_STEP_SIZE: number = 0.001 + + /** + * Default step size reduction. + */ + public static readonly DEFAULT_STEP_SIZE_REDUCTION: number = 0.75 + + /** + * Default required number of quality value improvements. + */ + public static readonly DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS: number = 5 + + /** + * Maximum number of iterations. + */ + protected maxNIterations!: number + + /** + * Initial step size. + */ + protected initialStepSize!: number + + /** + * Minimum step size. + */ + protected minStepSize!: number + + /** + * Step size reduction. + */ + protected stepSizeReduction!: number + + /** + * Required number of quality value improvements. + */ + protected requiredNQualityValueImprovements!: number + + /** + * Random number generator. + */ + protected random!: Random + + /** + * Constructs a gradient descent VOS layout algorithm. + */ + public constructor () { + super() + this.initializeBasedOnRandom(new Random()) + } + + /** + * Initializes a gradient descent VOS layout algorithm. + * + * @param random Random number generator + */ + public initializeBasedOnRandom (random: Random): void { + this.initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndRandom(GradientDescentVOSLayoutAlgorithm.DEFAULT_ATTRACTION, GradientDescentVOSLayoutAlgorithm.DEFAULT_REPULSION, GradientDescentVOSLayoutAlgorithm.DEFAULT_EDGE_WEIGHT_INCREMENT, random) + } + + /** + * Initializes a gradient descent VOS layout algorithm for a specified + * attraction parameter, repulsion parameter, and edge weight increment + * parameter. + * + * @param attraction Attraction parameter + * @param repulsion Repulsion parameter + * @param edgeWeightIncrement Edge weight increment parameter + * @param random Random number generator + */ + public initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndRandom (attraction: number, repulsion: number, edgeWeightIncrement: number, random: Random): void { + this.initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndMaxNIterationsAndInitialStepSizeAndMinStepSizeAndStepSizeReductionAndRequiredNQualityValueImprovementsAndRandom(attraction, repulsion, edgeWeightIncrement, GradientDescentVOSLayoutAlgorithm.DEFAULT_MAX_N_ITERATIONS, GradientDescentVOSLayoutAlgorithm.DEFAULT_INITIAL_STEP_SIZE, GradientDescentVOSLayoutAlgorithm.DEFAULT_MIN_STEP_SIZE, GradientDescentVOSLayoutAlgorithm.DEFAULT_STEP_SIZE_REDUCTION, GradientDescentVOSLayoutAlgorithm.DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS, random) + } + + /** + * Initializes a gradient descent VOS layout algorithm for a specified + * attraction parameter, repulsion parameter, edge weight increment + * parameter, maximum number of iterations, initial step size, minimum step + * size, step size reduction, and required number of quality value + * improvements. + * + * @param attraction Attraction parameter + * @param repulsion Repulsion parameter + * @param edgeWeightIncrement Edge weight increment parameter + * @param maxNIterations Maximum number of iterations + * @param initialStepSize Initial step size + * @param minStepSize Minimum step size + * @param stepSizeReduction Step size reduction + * @param requiredNQualityValueImprovements Required number of quality value + * improvements + * @param random Random number generator + */ + public initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndMaxNIterationsAndInitialStepSizeAndMinStepSizeAndStepSizeReductionAndRequiredNQualityValueImprovementsAndRandom (attraction: number, repulsion: number, edgeWeightIncrement: number, maxNIterations: number, initialStepSize: number, minStepSize: number, stepSizeReduction: number, requiredNQualityValueImprovements: number, random: Random): void { + super.initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrement(attraction, repulsion, edgeWeightIncrement) + + this.maxNIterations = maxNIterations + this.initialStepSize = initialStepSize + this.minStepSize = minStepSize + this.stepSizeReduction = stepSizeReduction + this.requiredNQualityValueImprovements = requiredNQualityValueImprovements + this.random = random + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public clone (): GradientDescentVOSLayoutAlgorithm { + const clonedAlgorithm = new GradientDescentVOSLayoutAlgorithm() + clonedAlgorithm.attraction = this.attraction + clonedAlgorithm.repulsion = this.repulsion + clonedAlgorithm.edgeWeightIncrement = this.edgeWeightIncrement + clonedAlgorithm.maxNIterations = this.maxNIterations + clonedAlgorithm.initialStepSize = this.initialStepSize + clonedAlgorithm.minStepSize = this.minStepSize + clonedAlgorithm.stepSizeReduction = this.stepSizeReduction + clonedAlgorithm.requiredNQualityValueImprovements = this.requiredNQualityValueImprovements + return clonedAlgorithm + } + + /** + * Returns the maximum number of iterations. + * + * @return Maximum number of iterations + */ + public getMaxNIterations (): number { + return this.maxNIterations + } + + /** + * Returns the initial step size. + * + * @return Initial step size + */ + public getInitialStepSize (): number { + return this.initialStepSize + } + + /** + * Returns the minimum step size. + * + * @return Minimum step size + */ + public getMinStepSize (): number { + return this.minStepSize + } + + /** + * Returns the step size reduction. + * + * @return Step size reduction + */ + public getStepSizeReduction (): number { + return this.stepSizeReduction + } + + /** + * Returns the required number of quality value improvements. + * + * @return Required number of quality value improvements + */ + public getRequiredNQualityValueImprovements (): number { + return this.requiredNQualityValueImprovements + } + + /** + * Sets the maximum number of iterations. + * + * @param maxNIterations Maximum number of iterations + */ + public setMaxNIterations (maxNIterations: number): void { + this.maxNIterations = maxNIterations + } + + /** + * Sets the initial step size. + * + * @param initialStepSize Initial step size + */ + public setInitialStepSize (initialStepSize: number): void { + this.initialStepSize = initialStepSize + } + + /** + * Sets the minimum step size. + * + * @param minStepSize Minimum step size + */ + public setMinStepSize (minStepSize: number): void { + this.minStepSize = minStepSize + } + + /** + * Sets the step size reduction. + * + * @param stepSizeReduction Step size reduction + */ + public setStepSizeReduction (stepSizeReduction: number): void { + this.stepSizeReduction = stepSizeReduction + } + + /** + * Sets the required number of quality value improvements. + * + * @param requiredNQualityValueImprovements Required number of quality value + * improvements + */ + public setRequiredNQualityValueImprovements (requiredNQualityValueImprovements: number): void { + this.requiredNQualityValueImprovements = requiredNQualityValueImprovements + } + + /** + * Finds a layout using the gradient descent VOS layout algorithm. + * + * @param network Network + * + * @return Layout + */ + public findLayout (network: Network): Layout { + const layout = new Layout({ nNodes: network.getNNodes(), random: this.random } as LayoutConstructorParametersWithNNodesAndRandom) + this.improveLayout(network, layout) + return layout + } + + /** + * Improves a layout using the gradient descent VOS layout algorithm. + * + * @param network Network + * @param layout Layout + */ + public improveLayout (network: Network, layout: Layout): void { + const nodeOrder = generateRandomPermutation(network.nNodes, this.random) + + let stepSize = this.initialStepSize + let qualityValue = Number.POSITIVE_INFINITY + let nQualityValueImprovements = 0 + const visitedNodes = new Array(network.nNodes) + let i = 0 + let a: number + while ((i < this.maxNIterations) && (stepSize >= this.minStepSize)) { + const oldQualityValue = qualityValue + qualityValue = 0 + visitedNodes.fill(false) + for (let j = 0; j < network.nNodes; j++) { + const k = nodeOrder[j] + + let gradient1 = 0 + let gradient2 = 0 + + for (let l = network.firstNeighborIndices[k]; l < network.firstNeighborIndices[k + 1]; l++) { + const distance1 = layout.coordinates[0][k] - layout.coordinates[0][network.neighbors[l]] + const distance2 = layout.coordinates[1][k] - layout.coordinates[1][network.neighbors[l]] + const squaredDistance = distance1 * distance1 + distance2 * distance2 + + const distance = Math.sqrt(squaredDistance) + a = fastPow(distance, this.attraction) + + if (squaredDistance > 0) { + const b = network.edgeWeights[l] * a / squaredDistance + gradient1 += b * distance1 + gradient2 += b * distance2 + } + + if (!visitedNodes[network.neighbors[l]]) { + if (this.attraction !== 0) { + qualityValue += network.edgeWeights[l] * a / this.attraction + } else { + qualityValue += network.edgeWeights[l] * Math.log(distance) + } + } + } + + for (let l = 0; l < network.nNodes; l++) { + if (l !== k) { + const distance1 = layout.coordinates[0][k] - layout.coordinates[0][l] + const distance2 = layout.coordinates[1][k] - layout.coordinates[1][l] + const squaredDistance = distance1 * distance1 + distance2 * distance2 + const distance = Math.sqrt(squaredDistance) + + a = fastPow(distance, this.repulsion) + + if (squaredDistance > 0) { + const b = network.nodeWeights[k] * network.nodeWeights[l] * a / squaredDistance + gradient1 -= b * distance1 + gradient2 -= b * distance2 + } + + if (!visitedNodes[l]) { + if (this.repulsion !== 0) { + qualityValue -= network.nodeWeights[k] * network.nodeWeights[l] * a / this.repulsion + } else { + qualityValue -= network.nodeWeights[k] * network.nodeWeights[l] * Math.log(distance) + } + } + + if (this.edgeWeightIncrement > 0) { + a = fastPow(distance, this.attraction) + + if (squaredDistance > 0) { + const b = this.edgeWeightIncrement * a / squaredDistance + gradient1 += b * distance1 + gradient2 += b * distance2 + } + + if (!visitedNodes[l]) { + if (this.attraction !== 0) { + qualityValue += this.edgeWeightIncrement * a / this.attraction + } else { + qualityValue += this.edgeWeightIncrement * Math.log(distance) + } + } + } + } + } + + const gradientLength = Math.sqrt(gradient1 * gradient1 + gradient2 * gradient2) + layout.coordinates[0][k] -= stepSize * gradient1 / gradientLength + layout.coordinates[1][k] -= stepSize * gradient2 / gradientLength + + visitedNodes[k] = true + } + + if (qualityValue < oldQualityValue) { + nQualityValueImprovements++ + if (nQualityValueImprovements >= this.requiredNQualityValueImprovements) { + stepSize /= this.stepSizeReduction + nQualityValueImprovements = 0 + } + } else { + stepSize *= this.stepSizeReduction + nQualityValueImprovements = 0 + } + + i++ + } + } +} diff --git a/src/incrementalCPMClusteringAlgorithm.ts b/src/incrementalCPMClusteringAlgorithm.ts new file mode 100644 index 0000000..19b4f96 --- /dev/null +++ b/src/incrementalCPMClusteringAlgorithm.ts @@ -0,0 +1,54 @@ +import CPMClusteringAlgorithm from './CPMClusteringAlgorithm' +import IncrementalClusteringAlgorithm from './incrementalClusteringAlgorithm' +import Clustering, { ClusteringParametersWithNNodes } from './clustering' +import Network from './network' + +/** + * Abstract base class for incremental clustering algorithms that use the CPM + * quality function. + */ +export default abstract class IncrementalCPMClusteringAlgorithm extends CPMClusteringAlgorithm implements IncrementalClusteringAlgorithm { + /** + * Constructs an incremental CPM clustering algorithm. + */ + public constructor () { + super() + this.initializeBasedOnResolution(IncrementalCPMClusteringAlgorithm.DEFAULT_RESOLUTION) + } + + /** + * Initializes an incremental CPM clustering algorithm with a specified + * resolution parameter. + * + * @param resolution Resolution parameter + */ + public initializeBasedOnResolution (resolution: number): void { + super.initializeBasedOnResolution(resolution) + } + + /** + * Finds a clustering of the nodes in a network. + * + * The clustering is obtained by calling {@link improveClustering} and by + * providing a singleton clustering as input to this method. + * + * @param network Network + * + * @return Clustering + */ + public findClustering (network: Network): Clustering { + const clustering = new Clustering({ nNodes: network.getNNodes() } as ClusteringParametersWithNNodes) + this.improveClustering(network, clustering) + return clustering + } + + /** + * Improves a clustering of the nodes in a network. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + public abstract improveClustering (network: Network, clustering: Clustering): boolean +} diff --git a/src/incrementalClusteringAlgorithm.ts b/src/incrementalClusteringAlgorithm.ts new file mode 100644 index 0000000..dddb6d5 --- /dev/null +++ b/src/incrementalClusteringAlgorithm.ts @@ -0,0 +1,19 @@ +import ClusteringAlgorithm from './clusteringAlgorithm' +import Clustering from './clustering' +import Network from './network' + +/** + * Interface for clustering algorithms that are able to improve an existing + * clustering. + */ +export default interface IncrementalClusteringAlgorithm extends ClusteringAlgorithm { + /** + * Improves a clustering of the nodes in a network. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + improveClustering (network: Network, clustering: Clustering): boolean +} diff --git a/src/index.ts b/src/index.ts new file mode 100644 index 0000000..4a0ac4d --- /dev/null +++ b/src/index.ts @@ -0,0 +1,56 @@ +/** + * Provides data structures and algorithms for network analysis. + * + * The classes {@link Network}, {@link Clustering}, and {@link Layout} + * represent the core data structures. The classes {@link LeidenAlgorithm} and + * {@link LouvainAlgorithm} represent the core algorithms for network + * clustering. The class {@link GradientDescentVOSLayoutAlgorithm} represents + * the core algorithm for network layout. + * + * @module + */ +import Clustering, { ClusteringParametersWithClusters, ClusteringParametersWithNNodes } from './clustering' +import ClusteringAlgorithm from './clusteringAlgorithm' +import CPMClusteringAlgorithm from './CPMClusteringAlgorithm' +import ComponentsAlgorithm from './componentsAlgorithm' +import FastLocalMovingAlgorithm from './fastLocalMovingAlgorithm' +import GradientDescentVOSLayoutAlgorithm from './gradientDescentVOSLayoutAlgorithm' +import IncrementalClusteringAlgorithm from './incrementalClusteringAlgorithm' +import IncrementalCPMClusteringAlgorithm from './incrementalCPMClusteringAlgorithm' +import IterativeCPMClusteringAlgorithm from './iterativeCPMClusteringAlgorithm' +import Layout, { LayoutConstructorParametersWithCoordinates, LayoutConstructorParametersWithNNodes, LayoutConstructorParametersWithNNodesAndRandom } from './layout' +import LeidenAlgorithm from './leidenAlgorithm' +import LocalMergingAlgorithm from './localMergingAlgorithm' +import LouvainAlgorithm from './louvainAlgorithm' +import Network, { NetworkConstructorParameters } from './network' +import QualityClusteringAlgorithm from './qualityClusteringAlgorithm' +import QualityLayoutAlgorithm from './qualityLayoutAlgorithm' +import StandardLocalMovingAlgorithm from './standardLocalMovingAlgorithm' +import VOSLayoutAlgorithm from './VOSLayoutAlgorithm' + +export { + Clustering, + ClusteringParametersWithClusters, + ClusteringParametersWithNNodes, + ClusteringAlgorithm, + CPMClusteringAlgorithm, + ComponentsAlgorithm, + FastLocalMovingAlgorithm, + GradientDescentVOSLayoutAlgorithm, + IncrementalClusteringAlgorithm, + IncrementalCPMClusteringAlgorithm, + IterativeCPMClusteringAlgorithm, + Layout, + LayoutConstructorParametersWithCoordinates, + LayoutConstructorParametersWithNNodes, + LayoutConstructorParametersWithNNodesAndRandom, + LeidenAlgorithm, + LocalMergingAlgorithm, + LouvainAlgorithm, + Network, + NetworkConstructorParameters, + QualityClusteringAlgorithm, + QualityLayoutAlgorithm, + StandardLocalMovingAlgorithm, + VOSLayoutAlgorithm, +} diff --git a/src/iterativeCPMClusteringAlgorithm.ts b/src/iterativeCPMClusteringAlgorithm.ts new file mode 100644 index 0000000..cd9ecc0 --- /dev/null +++ b/src/iterativeCPMClusteringAlgorithm.ts @@ -0,0 +1,97 @@ +import IncrementalCPMClusteringAlgorithm from './incrementalCPMClusteringAlgorithm' +import Clustering from './clustering' +import Network from './network' + +/** + * Abstract base class for iterative clustering algorithms that use the CPM + * quality function. + */ +export default abstract class IterativeCPMClusteringAlgorithm extends IncrementalCPMClusteringAlgorithm { + /** + * Default number of iterations. + */ + public static readonly DEFAULT_N_ITERATIONS: number = 1 + + /** + * Number of iterations. + */ + protected nIterations!: number + + /** + * Constructs an iterative CPM clustering algorithm. + */ + public constructor () { + super() + this.initializeBasedOnResolutionAndNIterations(IterativeCPMClusteringAlgorithm.DEFAULT_RESOLUTION, IterativeCPMClusteringAlgorithm.DEFAULT_N_ITERATIONS) + } + + /** + * Initializes an iterative CPM clustering algorithm with a specified + * resolution parameter and number of iterations. + * + * @param resolution Resolution parameter + * @param nIterations Number of iterations + */ + public initializeBasedOnResolutionAndNIterations (resolution: number, nIterations: number): void { + super.initializeBasedOnResolution(resolution) + this.nIterations = nIterations + } + + /** + * Returns the number of iterations. + * + * @return Number of iterations + */ + public getNIterations (): number { + return this.nIterations + } + + /** + * Sets the number of iterations. + * + * @param nIterations Number of iterations + */ + public setNIterations (nIterations: number): void { + this.nIterations = nIterations + } + + /** + * Improves a clustering of the nodes in a network. + * + * If the number of iterations `nIterations` is positive, the clustering is + * improved by making `nIterations` calls to + * {@link improveClusteringOneIteration}. If `nIterations` equals 0, calls to + * {@link #improveClusteringOneIteration} continue to be made until there has + * been a call that did not result in an improvement of the clustering. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + public improveClustering (network: Network, clustering: Clustering): boolean { + let update = false + if (this.nIterations > 0) { + for (let i = 0; i < this.nIterations; i++) { + const update2 = this.improveClusteringOneIteration(network, clustering) + update ||= update2 + } + } else { + while (this.improveClusteringOneIteration(network, clustering)) { + update = true + } + } + return update + } + + /** + * Improves a clustering by performing one iteration of an iterative + * clustering algorithm. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + protected abstract improveClusteringOneIteration (network: Network, clustering: Clustering): boolean +} diff --git a/src/layout.ts b/src/layout.ts new file mode 100644 index 0000000..f081a7c --- /dev/null +++ b/src/layout.ts @@ -0,0 +1,320 @@ +import Random from 'java-random' +import { calcAverage, calcMedian, calcMinimum, calcMaximum } from './utils/arrays' + +/** + * Interface for specifying the number of nodes when constructing a layout. + */ +export interface LayoutConstructorParametersWithNNodes { + /** + * Number of nodes. + */ + nNodes: number +} + +/** + * Interface for specifying the number of nodes and the random number generator + * when constructing a layout. + */ +export interface LayoutConstructorParametersWithNNodesAndRandom { + /** + * Number of nodes. + */ + nNodes: number + + /** + * Random number generator. + */ + random: Random +} + +/** + * Interface for specifying the coordinates for each node when constructing a + * layout. + */ +export interface LayoutConstructorParametersWithCoordinates { + /** + * Coordinates of each node. + */ + coordinates: number[][] +} + +/** + * Layout of the nodes in a network. + */ +export default class Layout { + /** + * Coordinates of each node. + */ + public coordinates!: number[][] + + /** + * Number of nodes. + */ + protected nNodes!: number + + /** + * Constructs a layout. + * + * @param parameters Layout constructor parameters. + */ + public constructor (parameters: LayoutConstructorParametersWithNNodes | LayoutConstructorParametersWithNNodesAndRandom | LayoutConstructorParametersWithCoordinates) { + if (typeof (parameters as LayoutConstructorParametersWithNNodesAndRandom).random !== 'undefined') { + const layoutParameters = parameters as LayoutConstructorParametersWithNNodesAndRandom + this.initializeBasedOnNNodesAndRandom(layoutParameters.nNodes, layoutParameters.random) + } else if (typeof (parameters as LayoutConstructorParametersWithNNodes).nNodes !== 'undefined') { + const layoutParameters = parameters as LayoutConstructorParametersWithNNodes + this.initializeBasedOnNNodes(layoutParameters.nNodes) + } else if (typeof (parameters as LayoutConstructorParametersWithCoordinates).coordinates !== 'undefined') { + const layoutParameters = parameters as LayoutConstructorParametersWithCoordinates + this.initializeBasedOnCoordinates(layoutParameters.coordinates) + } + } + + /** + * Initializes a random layout for a specified number of nodes. + * + * @param nNodes Number of nodes + */ + public initializeBasedOnNNodes (nNodes: number): void { + this.initializeBasedOnNNodesAndRandom(nNodes, new Random()) + } + + /** + * Initializes a random layout for a specified number of nodes. + * + * @param nNodes Number of nodes + * @param random Random number generator + */ + public initializeBasedOnNNodesAndRandom (nNodes: number, random: Random): void { + this.nNodes = nNodes + this.coordinates = new Array>(2) + this.coordinates[0] = new Array(nNodes) + this.coordinates[1] = new Array(nNodes) + this.initRandomCoordinatesHelper(random) + } + + /** + * Initializes a layout using specified coordinates for each node. + * + * @param coordinates Coordinates of each node + */ + public initializeBasedOnCoordinates (coordinates: number[][]): void { + this.nNodes = coordinates[0].length + this.coordinates = new Array>(2) + this.coordinates[0] = coordinates[0].slice() + this.coordinates[1] = coordinates[1].slice() + } + + /** + * Clones the layout. + * + * @return Cloned layout + */ + public clone (): Layout { + const clonedLayout = new Layout({ nNodes: this.nNodes } as LayoutConstructorParametersWithNNodes) + clonedLayout.coordinates = new Array>(2) + clonedLayout.coordinates[0] = this.coordinates[0].slice() + clonedLayout.coordinates[1] = this.coordinates[1].slice() + return clonedLayout + } + + /** + * Returns the number of nodes. + * + * @return Number of nodes + */ + public getNNodes (): number { + return this.nNodes + } + + /** + * Returns the coordinates of each node. + * + * @return Coordinates of each node + */ + public getCoordinates (): number[][] { + const clonedCoordinates = new Array>(2) + clonedCoordinates[0] = this.coordinates[0].slice() + clonedCoordinates[1] = this.coordinates[1].slice() + return clonedCoordinates + } + + /** + * Returns the coordinates of a node. + * + * @param node Node + * + * @return Coordinates + */ + public getCoordinatesNode (node: number): number[] { + const coordinates = new Array(2) + coordinates[0] = this.coordinates[0][node] + coordinates[1] = this.coordinates[1][node] + return coordinates + } + + /** + * Returns the minimum of the coordinates of all node. + * + * @return Minimum coordinates + */ + public getMinCoordinates (): number[] { + const minCoordinates = Array(2) + minCoordinates[0] = calcMinimum(this.coordinates[0]) + minCoordinates[1] = calcMinimum(this.coordinates[1]) + return minCoordinates + } + + /** + * Returns the maximum of the coordinates of all node. + * + * @return Maximum coordinates + */ + public getMaxCoordinates (): number[] { + const maxCoordinates = Array(2) + maxCoordinates[0] = calcMaximum(this.coordinates[0]) + maxCoordinates[1] = calcMaximum(this.coordinates[1]) + return maxCoordinates + } + + /** + * Returns the average distance between all pairs of nodes. + * + * @return Average distance + */ + public getAverageDistance (): number { + let averageDistance = 0 + for (let i = 0; i < this.nNodes; i++) { + for (let j = 0; j < i; j++) { + const distance1 = this.coordinates[0][i] - this.coordinates[0][j] + const distance2 = this.coordinates[1][i] - this.coordinates[1][j] + averageDistance += Math.sqrt(distance1 * distance1 + distance2 * distance2) + } + } + averageDistance /= this.nNodes * (this.nNodes - 1) / 2 + return averageDistance + } + + /** + * Positions a node at coordinates. + * + * @param node Node + * @param coordinates Coordinates + */ + public setCoordinates (node: number, coordinates: number[]): void { + this.coordinates[0][node] = coordinates[0] + this.coordinates[1][node] = coordinates[1] + } + + /** + * Initializes a random layout. + * + * Each node is positioned at random coordinates. + * + * @param random Random number generator + */ + public initRandomCoordinates (random: Random = new Random()): void { + this.initRandomCoordinatesHelper(random) + } + + /** + * Standardizes a layout. + * + * Standardization involves translation, rotation, reflection, and + * optionally dilation. The layout is translated so that it is centered at + * the origin. The layout is rotated so that the variance in the horizontal + * dimension is maximized. The layout is reflected so that in both the + * horizontal and the vertical dimension the median of the coordinates is + * non-positive. If `standardizeDistances = true`, the layout is dilated so + * that the average distance between nodes equals one. + * + * @param standardizeDistances Standardize distances + */ + public standardize (standardizeDistances: boolean): void { + const averageCoordinate1 = calcAverage(this.coordinates[0]) + const averageCoordinate2 = calcAverage(this.coordinates[1]) + for (let i = 0; i < this.nNodes; i++) { + this.coordinates[0][i] -= averageCoordinate1 + this.coordinates[1][i] -= averageCoordinate2 + } + + let variance1 = 0 + let variance2 = 0 + let covariance = 0 + for (let i = 0; i < this.nNodes; i++) { + variance1 += this.coordinates[0][i] * this.coordinates[0][i] + variance2 += this.coordinates[1][i] * this.coordinates[1][i] + covariance += this.coordinates[0][i] * this.coordinates[1][i] + } + variance1 /= this.nNodes + variance2 /= this.nNodes + covariance /= this.nNodes + const discriminant = variance1 * variance1 + variance2 * variance2 - 2 * variance1 * variance2 + 4 * covariance * covariance + const eigenvalue1 = (variance1 + variance2 - Math.sqrt(discriminant)) / 2 + const eigenvalue2 = (variance1 + variance2 + Math.sqrt(discriminant)) / 2 + let normalizedEigenvector11 = variance1 + covariance - eigenvalue1 + let normalizedEigenvector12 = variance2 + covariance - eigenvalue1 + let vectorLength = Math.sqrt(normalizedEigenvector11 * normalizedEigenvector11 + normalizedEigenvector12 * normalizedEigenvector12) + normalizedEigenvector11 /= vectorLength + normalizedEigenvector12 /= vectorLength + let normalizedEigenvector21 = variance1 + covariance - eigenvalue2 + let normalizedEigenvector22 = variance2 + covariance - eigenvalue2 + vectorLength = Math.sqrt(normalizedEigenvector21 * normalizedEigenvector21 + normalizedEigenvector22 * normalizedEigenvector22) + normalizedEigenvector21 /= vectorLength + normalizedEigenvector22 /= vectorLength + for (let i = 0; i < this.nNodes; i++) { + const coordinateOld1 = this.coordinates[0][i] + const coordinateOld2 = this.coordinates[1][i] + this.coordinates[0][i] = normalizedEigenvector11 * coordinateOld1 + normalizedEigenvector12 * coordinateOld2 + this.coordinates[1][i] = normalizedEigenvector21 * coordinateOld1 + normalizedEigenvector22 * coordinateOld2 + } + + for (let i = 0; i < 2; i++) { + if (calcMedian(this.coordinates[i]) > 0) { + this.flip(i) + } + } + + if (standardizeDistances) { + const averageDistance = this.getAverageDistance() + for (let i = 0; i < this.nNodes; i++) { + this.coordinates[0][i] /= averageDistance + this.coordinates[1][i] /= averageDistance + } + } + } + + /** + * Rotates a layout. + * + * @param angle Angle + */ + public rotate (angle: number): void { + const sin = Math.sin(-angle * Math.PI / 180) + const cos = Math.cos(-angle * Math.PI / 180) + for (let i = 0; i < this.nNodes; i++) { + const coordinateOld1 = this.coordinates[0][i] + const coordinateOld2 = this.coordinates[1][i] + this.coordinates[0][i] = cos * coordinateOld1 - sin * coordinateOld2 + this.coordinates[1][i] = sin * coordinateOld1 + cos * coordinateOld2 + } + } + + /** + * Flips a layout. + * + * @param dimension Dimension + */ + public flip (dimension: number): void { + for (let i = 0; i < this.nNodes; i++) { + this.coordinates[dimension][i] *= -1 + } + } + + private initRandomCoordinatesHelper (random: Random): void { + for (let i = 0; i < this.nNodes; i++) { + this.coordinates[0][i] = 2 * random.nextDouble() - 1 + this.coordinates[1][i] = 2 * random.nextDouble() - 1 + } + } +} diff --git a/src/layoutAlgorithm.ts b/src/layoutAlgorithm.ts new file mode 100644 index 0000000..be3adf8 --- /dev/null +++ b/src/layoutAlgorithm.ts @@ -0,0 +1,16 @@ +import Layout from './layout' +import Network from './network' + +/** + * Interface for layout algorithms. + */ +export default interface LayoutAlgorithm { + /** + * Finds a layout of the nodes in a network. + * + * @param network Network + * + * @return Layout + */ + findLayout (network: Network): Layout +} diff --git a/src/leidenAlgorithm.ts b/src/leidenAlgorithm.ts new file mode 100644 index 0000000..04c7c16 --- /dev/null +++ b/src/leidenAlgorithm.ts @@ -0,0 +1,261 @@ +import Random from 'java-random' +import IterativeCPMClusteringAlgorithm from './iterativeCPMClusteringAlgorithm' +import IncrementalCPMClusteringAlgorithm from './incrementalCPMClusteringAlgorithm' +import LocalMergingAlgorithm from './localMergingAlgorithm' +import FastLocalMovingAlgorithm from './fastLocalMovingAlgorithm' +import Clustering, { ClusteringParametersWithNNodes } from './clustering' +import Network from './network' + +/** + * Leiden algorithm. + * + * The Leiden algorithm consists of three phases: + * + *
    + *
  1. local moving of nodes between clusters,
    2. + *
  2. refinement of the clusters,
    3. + *
  3. aggregation of the network based on the refined clusters, using the + * non-refined clusters to create an initial clustering for the aggregate + * network.
    4. + *
+ * + * These phases are repeated until no further improvements can be made. By + * default, local moving of nodes is performed using the {@link + * FastLocalMovingAlgorithm}. + */ +export default class LeidenAlgorithm extends IterativeCPMClusteringAlgorithm { + /** + * Default randomness parameter. + */ + public static readonly DEFAULT_RANDOMNESS: number = LocalMergingAlgorithm.DEFAULT_RANDOMNESS + + /** + * Randomness parameter. + */ + protected randomness!: number + + /** + * Local moving algorithm. + */ + protected localMovingAlgorithm!: IncrementalCPMClusteringAlgorithm + + /** + * Random number generator. + */ + protected random!: Random + + /** + * Constructs a Leiden algorithm. + */ + public constructor () { + super() + this.initializeBasedOnRandom(new Random()) + } + + /** + * Initializes a Leiden algorithm. + * + * @param random Random number generator + */ + public initializeBasedOnRandom (random: Random): void { + this.initializeBasedOnResolutionAndNIterationsAndRandomnessAndRandom(LeidenAlgorithm.DEFAULT_RESOLUTION, LeidenAlgorithm.DEFAULT_N_ITERATIONS, LeidenAlgorithm.DEFAULT_RANDOMNESS, random) + } + + /** + * Initializes a Leiden algorithm for a specified resolution parameter, + * number of iterations, and randomness parameter. + * + * @param resolution Resolution parameter + * @param nIterations Number of iterations + * @param randomness Randomness parameter + * @param random Random number generator + */ + public initializeBasedOnResolutionAndNIterationsAndRandomnessAndRandom (resolution: number, nIterations: number, randomness: number, random: Random): void { + const localMovingAlgorithm = new FastLocalMovingAlgorithm() + localMovingAlgorithm.initializeBasedOnRandom(random) + this.initializeBasedOnResolutionAndNIterationsAndRandomnessAndLocalMovingAlgorithmAndRandom(resolution, nIterations, randomness, localMovingAlgorithm, random) + } + + /** + * Initializes a Leiden algorithm for a specified resolution parameter, + * number of iterations, randomness parameter, and local moving algorithm. + * + * @param resolution Resolution parameter + * @param nIterations Number of iterations + * @param randomness Randomness parameter + * @param localMovingAlgorithm Local moving algorithm + * @param random Random number generator + */ + public initializeBasedOnResolutionAndNIterationsAndRandomnessAndLocalMovingAlgorithmAndRandom (resolution: number, nIterations: number, randomness: number, localMovingAlgorithm: IncrementalCPMClusteringAlgorithm, random: Random): void { + super.initializeBasedOnResolutionAndNIterations(resolution, nIterations) + this.randomness = randomness + this.random = random + this.setLocalMovingAlgorithm(localMovingAlgorithm) + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public clone (): LeidenAlgorithm { + const clonedAlgorithm = new LeidenAlgorithm() + clonedAlgorithm.resolution = this.resolution + clonedAlgorithm.nIterations = this.nIterations + clonedAlgorithm.randomness = this.randomness + clonedAlgorithm.localMovingAlgorithm = this.localMovingAlgorithm.clone() as IncrementalCPMClusteringAlgorithm + clonedAlgorithm.random = this.random + return clonedAlgorithm + } + + /** + * Returns the randomness parameter. + * + * @return Randomness parameter + */ + public getRandomness (): number { + return this.randomness + } + + /** + * Returns the local moving algorithm. + * + * @return Local moving algorithm + */ + public getLocalMovingAlgorithm (): IncrementalCPMClusteringAlgorithm { + return this.localMovingAlgorithm.clone() as IncrementalCPMClusteringAlgorithm + } + + /** + * Sets the resolution parameter. + * + * Also sets the resolution parameter for the local moving algorithm. + * + * @param resolution Resolution parameter + */ + public setResolution (resolution: number): void { + super.setResolution(resolution) + this.localMovingAlgorithm.setResolution(resolution) + } + + /** + * Sets the randomness parameter. + * + * @param randomness Randomness parameter + */ + public setRandomness (randomness: number): void { + this.randomness = randomness + } + + /** + * Sets the local moving algorithm. + * + * @param localMovingAlgorithm Local moving algorithm + */ + public setLocalMovingAlgorithm (localMovingAlgorithm: IncrementalCPMClusteringAlgorithm): void { + this.localMovingAlgorithm = localMovingAlgorithm.clone() as IncrementalCPMClusteringAlgorithm + this.localMovingAlgorithm.setResolution(this.resolution) + } + + /** + * Improves a clustering by performing one iteration of the Leiden + * algorithm. + * + * The Leiden algorithm consists of three phases: + * + *
    + *
  1. local moving of nodes between clusters,
    2. + *
  2. refinement of the clusters,
    3. + *
  3. aggregation of the network based on the refined clusters, using the + * non-refined clusters to create an initial clustering for the aggregate + * network.
    4. + *
+ * + * These phases are repeated until no further improvements can be made. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + protected improveClusteringOneIteration (network: Network, clustering: Clustering): boolean { + // Update the clustering by moving individual nodes between clusters. + let update = this.localMovingAlgorithm.improveClustering(network, clustering) + + /* + * Terminate the algorithm if each node is assigned to its own cluster. + * Otherwise create an aggregate network and recursively apply the + * algorithm to this network. + */ + if (clustering.nClusters < network.nNodes) { + /* + * Refine the clustering by iterating over the clusters and by + * trying to split up each cluster into multiple clusters. + */ + const localMergingAlgorithm = new LocalMergingAlgorithm() + localMergingAlgorithm.initializeBasedOnResolutionAndRandomnessAndRandom(this.resolution, this.randomness, this.random) + const subnetworks = network.createSubnetworks(clustering) + const nodesPerCluster = clustering.getNodesPerCluster() + const refinement = new Clustering({ nNodes: network.nNodes } as ClusteringParametersWithNNodes) + refinement.nClusters = 0 + for (let i = 0; i < subnetworks.length; i++) { + const clusteringSubnetwork = localMergingAlgorithm.findClustering(subnetworks[i]) + + for (let j = 0; j < subnetworks[i].nNodes; j++) { + refinement.clusters[nodesPerCluster[i][j]] = refinement.nClusters + clusteringSubnetwork.clusters[j] + } + + refinement.nClusters += clusteringSubnetwork.nClusters + } + + let reducedNetwork: Network + let clusteringReducedNetwork: Clustering + if (refinement.nClusters < network.nNodes) { + /* + * Create an aggregate network based on the refined clustering of + * the non-aggregate network. + */ + reducedNetwork = network.createReducedNetwork(refinement) + /* + * Create an initial clustering for the aggregate network based + * on the non-refined clustering of the non-aggregate network. + */ + clusteringReducedNetwork = new Clustering({ nNodes: refinement.nClusters } as ClusteringParametersWithNNodes) + clusteringReducedNetwork.nClusters = clustering.nClusters + for (let i = 0; i < network.nNodes; i++) { + clusteringReducedNetwork.clusters[refinement.clusters[i]] = clustering.clusters[i] + } + + /* + * Set the non-refined clustering to the refined clustering, so that the results correctly + * merged back after recursively applying the algorithm to the aggregate network. + */ + clustering.clusters = refinement.clusters + clustering.nClusters = refinement.nClusters + } else { + /* + * The refinement is a singleton clustering, so we now + * aggregate on the basis of the non-refined clustering + * of the non-aggregate network. + */ + reducedNetwork = network.createReducedNetwork(clustering) + clusteringReducedNetwork = new Clustering({ nNodes: reducedNetwork.nNodes } as ClusteringParametersWithNNodes) + } + /* + * Recursively apply the algorithm to the aggregate network, + * starting from the initial clustering created for this network. + */ + const update2 = this.improveClusteringOneIteration(reducedNetwork, clusteringReducedNetwork) + update ||= update2 + + /* + * Update the clustering of the non-aggregate network so that it + * coincides with the final clustering obtained for the aggregate + * network. + */ + clustering.mergeClusters(clusteringReducedNetwork) + } + + return update + } +} diff --git a/src/localMergingAlgorithm.ts b/src/localMergingAlgorithm.ts new file mode 100644 index 0000000..a6ec5cb --- /dev/null +++ b/src/localMergingAlgorithm.ts @@ -0,0 +1,270 @@ +import Random from 'java-random' +import CPMClusteringAlgorithm from './CPMClusteringAlgorithm' +import Clustering, { ClusteringParametersWithNNodes } from './clustering' +import Network from './network' +import { generateRandomPermutation } from './utils/arrays' +import { fastExp } from './utils/fastMath' + +/** + * Local merging algorithm. + * + * The local merging algorithm starts from a singleton partition. It performs a + * single iteration over the nodes in a network. Each node belonging to a + * singleton cluster is considered for merging with another cluster. This + * cluster is chosen randomly from all clusters that do not result in a + * decrease in the quality function. The larger the increase in the quality + * function, the more likely a cluster is to be chosen. The strength of this + * effect is determined by the randomness parameter. The higher the value of + * the randomness parameter, the stronger the randomness in the choice of a + * cluster. The lower the value of the randomness parameter, the more likely + * the cluster resulting in the largest increase in the quality function is to + * be chosen. A node is merged with a cluster only if both are sufficiently + * well connected to the rest of the network. + * + * The local merging algorithm is used in the cluster refinement phase of the + * {@link LeidenAlgorithm}. + */ +export default class LocalMergingAlgorithm extends CPMClusteringAlgorithm { + /** + * Default randomness parameter. + */ + public static readonly DEFAULT_RANDOMNESS: number = 1e-2 + + /** + * Randomness parameter. + */ + protected randomness!: number + + /** + * Random number generator. + */ + protected random!: Random + + /** + * Constructs a local merging algorithm. + */ + public constructor () { + super() + this.initializeBasedOnRandom(new Random()) + } + + /** + * Initializes a local merging algorithm. + * + * @param random Random number generator + */ + public initializeBasedOnRandom (random: Random): void { + this.initializeBasedOnResolutionAndRandomnessAndRandom(LocalMergingAlgorithm.DEFAULT_RESOLUTION, LocalMergingAlgorithm.DEFAULT_RANDOMNESS, random) + } + + /** + * Initializes a local merging algorithm for a specified resolution + * parameter and randomness parameter. + * + * @param resolution Resolution parameter + * @param randomness Randomness parameter + * @param random Random number generator + */ + public initializeBasedOnResolutionAndRandomnessAndRandom (resolution: number, randomness: number, random: Random): void { + super.initializeBasedOnResolution(resolution) + this.randomness = randomness + this.random = random + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public clone (): LocalMergingAlgorithm { + const clonedAlgorithm = new LocalMergingAlgorithm() + clonedAlgorithm.resolution = this.resolution + clonedAlgorithm.randomness = this.randomness + clonedAlgorithm.random = this.random + return clonedAlgorithm + } + + /** + * Returns the randomness parameter. + * + * @return Randomness + */ + public getRandomness (): number { + return this.randomness + } + + /** + * Sets the randomness parameter. + * + * @param randomness Randomness + */ + public setRandomness (randomness: number): void { + this.randomness = randomness + } + + /** + * Finds a clustering of the nodes in a network using the local merging + * algorithm. + * + * The local merging algorithm starts from a singleton partition. It + * performs a single iteration over the nodes in a network. Each node + * belonging to a singleton cluster is considered for merging with another + * cluster. This cluster is chosen randomly from all clusters that do not + * result in a decrease in the quality function. The larger the increase in + * the quality function, the more likely a cluster is to be chosen. The + * strength of this effect is determined by the randomness parameter. The + * higher the value of the randomness parameter, the stronger the + * randomness in the choice of a cluster. The lower the value of the + * randomness parameter, the more likely the cluster resulting in the + * largest increase in the quality function is to be chosen. A node is + * merged with a cluster only if both are sufficiently well connected to + * the rest of the network. + * + * @param network Network + * + * @return Clustering + */ + public findClustering (network: Network): Clustering { + const clustering = new Clustering({ nNodes: network.nNodes } as ClusteringParametersWithNNodes) + + if (network.nNodes === 1) { + return clustering + } + + let update = false + + const totalNodeWeight = network.getTotalNodeWeight() + const clusterWeights = network.getNodeWeights() + const nonSingletonClusters = new Array(network.nNodes).fill(false) + const externalEdgeWeightPerCluster = network.getTotalEdgeWeightPerNode() + + const nodeOrder = generateRandomPermutation(network.nNodes, this.random) + + const edgeWeightPerCluster = new Array(network.nNodes).fill(0) + const neighboringClusters = new Array(network.nNodes).fill(0) + const cumTransformedQualityValueIncrementPerCluster = new Array(network.nNodes).fill(0) + for (let i = 0; i < network.nNodes; i++) { + const j = nodeOrder[i] + + /* + * Only nodes belonging to singleton clusters can be moved to a + * different cluster. This guarantees that clusters will never be + * split up. Additionally, only nodes that are well connected with + * the rest of the network are considered for moving. + */ + if (!nonSingletonClusters[j] && (externalEdgeWeightPerCluster[j] >= clusterWeights[j] * (totalNodeWeight - clusterWeights[j]) * this.resolution)) { + /* + * Remove the currently selected node from its current cluster. + * This causes the cluster to be empty. + */ + clusterWeights[j] = 0 + externalEdgeWeightPerCluster[j] = 0 + + /* + * Identify the neighboring clusters of the currently selected + * node, that is, the clusters with which the currently + * selected node is connected. The old cluster of the currently + * selected node is also included in the set of neighboring + * clusters. In this way, it is always possible that the + * currently selected node will be moved back to its old + * cluster. + */ + neighboringClusters[0] = j + let nNeighboringClusters = 1 + for (let k = network.firstNeighborIndices[j]; k < network.firstNeighborIndices[j + 1]; k++) { + const l = clustering.clusters[network.neighbors[k]] + if (edgeWeightPerCluster[l] === 0) { + neighboringClusters[nNeighboringClusters] = l + nNeighboringClusters++ + } + edgeWeightPerCluster[l] += network.edgeWeights[k] + } + + /* + * For each neighboring cluster of the currently selected node, + * determine whether the neighboring cluster is well connected + * with the rest of the network. For each neighboring cluster + * that is well connected, calculate the increment of the + * quality function obtained by moving the currently selected + * node to the neighboring cluster. For each neighboring + * cluster for which the increment is non-negative, calculate a + * transformed increment that will determine the probability + * with which the currently selected node is moved to the + * neighboring cluster. + */ + let bestCluster = j + let maxQualityValueIncrement = 0 + let totalTransformedQualityValueIncrement = 0 + for (let k = 0; k < nNeighboringClusters; k++) { + const l = neighboringClusters[k] + + if (externalEdgeWeightPerCluster[l] >= clusterWeights[l] * (totalNodeWeight - clusterWeights[l]) * this.resolution) { + const qualityValueIncrement = edgeWeightPerCluster[l] - network.nodeWeights[j] * clusterWeights[l] * this.resolution + + if (qualityValueIncrement > maxQualityValueIncrement) { + bestCluster = l + maxQualityValueIncrement = qualityValueIncrement + } + + if (qualityValueIncrement >= 0) { + totalTransformedQualityValueIncrement += fastExp(qualityValueIncrement / this.randomness) + } + } + + cumTransformedQualityValueIncrementPerCluster[k] = totalTransformedQualityValueIncrement + + edgeWeightPerCluster[l] = 0 + } + + /* + * Determine the neighboring cluster to which the currently + * selected node will be moved. + */ + let chosenCluster: number + if (totalTransformedQualityValueIncrement < Number.POSITIVE_INFINITY) { + const r = totalTransformedQualityValueIncrement * this.random.nextDouble() + let minIdx = -1 + let maxIdx = nNeighboringClusters + 1 + while (minIdx < maxIdx - 1) { + const midIdx = Math.trunc((minIdx + maxIdx) / 2) + if (cumTransformedQualityValueIncrementPerCluster[midIdx] >= r) { + maxIdx = midIdx + } else { + minIdx = midIdx + } + } + chosenCluster = neighboringClusters[maxIdx] + } else { + chosenCluster = bestCluster + } + + /* + * Move the currently selected node to its new cluster and + * update the clustering statistics. + */ + clusterWeights[chosenCluster] += network.nodeWeights[j] + + for (let k = network.firstNeighborIndices[j]; k < network.firstNeighborIndices[j + 1]; k++) { + if (clustering.clusters[network.neighbors[k]] === chosenCluster) { + externalEdgeWeightPerCluster[chosenCluster] -= network.edgeWeights[k] + } else { + externalEdgeWeightPerCluster[chosenCluster] += network.edgeWeights[k] + } + } + + if (chosenCluster !== j) { + clustering.clusters[j] = chosenCluster + + nonSingletonClusters[chosenCluster] = true + update = true + } + } + } + + if (update) { + clustering.removeEmptyClusters() + } + + return clustering + } +} diff --git a/src/louvainAlgorithm.ts b/src/louvainAlgorithm.ts new file mode 100644 index 0000000..91b0d04 --- /dev/null +++ b/src/louvainAlgorithm.ts @@ -0,0 +1,168 @@ +import Random from 'java-random' +import IterativeCPMClusteringAlgorithm from './iterativeCPMClusteringAlgorithm' +import IncrementalCPMClusteringAlgorithm from './incrementalCPMClusteringAlgorithm' +import StandardLocalMovingAlgorithm from './standardLocalMovingAlgorithm' +import Clustering, { ClusteringParametersWithNNodes } from './clustering' +import Network from './network' + +/** + * Louvain algorithm. + * + * The Louvain algorithm consists of two phases: + * + *
    + *
  1. local moving of nodes between clusters,
    2. + *
  2. aggregation of the network based on the clusters.
    3. + *
+ * + * These phases are repeated until no further improvements can be made. By + * default, local moving of nodes is performed using the {@link + * StandardLocalMovingAlgorithm}. + */ +export default class LouvainAlgorithm extends IterativeCPMClusteringAlgorithm { + /** + * Local moving algorithm. + */ + protected localMovingAlgorithm!: IncrementalCPMClusteringAlgorithm + + /** + * Constructs a Louvain algorithm. + */ + public constructor () { + super() + this.initializeBasedOnRandom(new Random()) + } + + /** + * Initializes a Louvain algorithm. + * + * @param random Random number generator + */ + public initializeBasedOnRandom (random: Random): void { + this.initializeBasedOnResolutionAndNIterationsAndRandom(LouvainAlgorithm.DEFAULT_RESOLUTION, LouvainAlgorithm.DEFAULT_N_ITERATIONS, random) + } + + /** + * Initializes a Louvain algorithm for a specified resolution parameter and + * number of iterations. + * + * @param resolution Resolution parameter + * @param nIterations Number of iterations + * @param random Random number generator + */ + public initializeBasedOnResolutionAndNIterationsAndRandom (resolution: number, nIterations: number, random: Random): void { + const localMovingAlgorithm = new StandardLocalMovingAlgorithm() + localMovingAlgorithm.initializeBasedOnRandom(random) + this.initializeBasedOnResolutionAndNIterationsAndLocalMovingAlgorithm(resolution, nIterations, localMovingAlgorithm) + } + + /** + * Initializes a Louvain algorithm for a specified resolution parameter, + * number of iterations, and local moving algorithm. + * + * @param resolution Resolution parameter + * @param nIterations Number of iterations + * @param localMovingAlgorithm Local moving algorithm + */ + public initializeBasedOnResolutionAndNIterationsAndLocalMovingAlgorithm (resolution: number, nIterations: number, localMovingAlgorithm: IncrementalCPMClusteringAlgorithm): void { + super.initializeBasedOnResolutionAndNIterations(resolution, nIterations) + this.setLocalMovingAlgorithm(localMovingAlgorithm) + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public clone (): LouvainAlgorithm { + const clonedAlgorithm = new LouvainAlgorithm() + clonedAlgorithm.resolution = this.resolution + clonedAlgorithm.nIterations = this.nIterations + clonedAlgorithm.localMovingAlgorithm = this.localMovingAlgorithm.clone() as IncrementalCPMClusteringAlgorithm + return clonedAlgorithm + } + + /** + * Returns the local moving algorithm. + * + * @return Local moving algorithm + */ + public getLocalMovingAlgorithm (): IncrementalCPMClusteringAlgorithm { + return this.localMovingAlgorithm.clone() as IncrementalCPMClusteringAlgorithm + } + + /** + * Sets the resolution parameter. + * + * Also sets the resolution parameter for the local moving algorithm. + * + * @param resolution Resolution parameter + */ + public setResolution (resolution: number): void { + super.setResolution(resolution) + this.localMovingAlgorithm.setResolution(resolution) + } + + /** + * Sets the local moving algorithm. + * + * @param localMovingAlgorithm Local moving algorithm + */ + public setLocalMovingAlgorithm (localMovingAlgorithm: IncrementalCPMClusteringAlgorithm): void { + this.localMovingAlgorithm = localMovingAlgorithm.clone() as IncrementalCPMClusteringAlgorithm + this.localMovingAlgorithm.setResolution(this.resolution) + } + + /** + * Improves a clustering by performing one iteration of the Louvain + * algorithm. + * + * The Louvain algorithm consists of two phases: + * + *
    + *
  1. local moving of nodes between clusters,
    2. + *
  2. aggregation of the network based on the clusters.
    3. + *
+ * + * These phases are repeated until no further improvements can be made. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + protected improveClusteringOneIteration (network: Network, clustering: Clustering): boolean { + // Update the clustering by moving individual nodes between clusters. + let update = this.localMovingAlgorithm.improveClustering(network, clustering) + + /* + * Terminate the algorithm if each node is assigned to its own cluster. + * Otherwise create an aggregate network and recursively apply the + * algorithm to this network. + */ + if (clustering.nClusters < network.nNodes) { + /* + * Create an aggregate network based on the clustering of the + * non-aggregate network. + */ + const reducedNetwork = network.createReducedNetwork(clustering) + + /* + * Recursively apply the algorithm to the aggregate network, + * starting from a singleton clustering. + */ + const reducedClustering = new Clustering({ nNodes: reducedNetwork.getNNodes() } as ClusteringParametersWithNNodes) + const update2 = this.improveClusteringOneIteration(reducedNetwork, reducedClustering) + update ||= update2 + + /* + * Update the clustering of the non-aggregate network so that it + * coincides with the final clustering obtained for the aggregate + * network. + */ + clustering.mergeClusters(reducedClustering) + } + + return update + } +} diff --git a/src/network.ts b/src/network.ts new file mode 100644 index 0000000..9dbb4d3 --- /dev/null +++ b/src/network.ts @@ -0,0 +1,999 @@ +import Random from 'java-random' +import ComponentsAlgorithm from './componentsAlgorithm' +import Clustering from './clustering' +import { calcSum, calcSumWithinRange, createDoubleArrayOfRandomNumbers, binarySearch } from './utils/arrays' + +/** + * Interface for specifying parameters when constructing a network. + */ +export interface NetworkConstructorParameters { + /** + * Number of nodes. + */ + nNodes?: number + + /** + * Indicates whether to set node weights equal to total edge weights. + */ + setNodeWeightsToTotalEdgeWeights?: boolean + + /** + * Node weights. + */ + nodeWeights?: number[] + + /** + * Edge list. + */ + edges?: number[][] + + /** + * Index of the first neighbor of each node. + */ + firstNeighborIndices?: number[] + + /** + * Neighbor list. + */ + neighbors?: number[] + + /** + * Edge weights. + */ + edgeWeights?: number[] + + /** + * Indicates whether the edge list is sorted. + */ + sortedEdges?: boolean + + /** + * Indicates whether to check the integrity of the network. + */ + checkIntegrity?: boolean +} + +/** + * Network. + * + * Weighted nodes and weighted edges are supported. Directed edges are not + * supported. + * + * Network objects are immutable. + * + * The adjacency matrix of the network is stored in a sparse compressed format. + */ +export default class Network { + /** + * Number of nodes. + */ + public nNodes!: number + + /** + * Node weights. + */ + public nodeWeights!: number[] + + /** + * Index of the first neighbor of each node in the `neighbors` array. + * + * The neighbors of node `i` are given by + * `neighbors[firstNeighborIndices[i]], ..., + * neighbors[firstNeighborIndices[i + 1] - 1]`. + */ + public firstNeighborIndices!: number[] + + /** + * Neighbors of each node. + */ + public neighbors!: number[] + + /** + * Edge weights. + */ + public edgeWeights!: number[] + + /** + * Total edge weight of self links. + */ + public totalEdgeWeightSelfLinks!: number + + /** + * Number of edges. + * + * Each edge is counted twice, once in each direction. + */ + protected nEdges!: number + + /** + * Constructs a network based on a list of edges or neighbors. + * + * @param parameters Network constructor parameters + */ + public constructor (parameters?: NetworkConstructorParameters) { + if (parameters) { + if (parameters.nodeWeights && parameters.edges) { + this.initializeNetworkBasedOnEdges(parameters.nodeWeights.length, parameters.nodeWeights, parameters.setNodeWeightsToTotalEdgeWeights, parameters.edges, parameters.edgeWeights, parameters.sortedEdges, parameters.checkIntegrity) + } else if (parameters.nodeWeights && parameters.firstNeighborIndices && parameters.neighbors) { + this.initializeNetworkBasedOnNeighbors(parameters.nodeWeights.length, parameters.nodeWeights, parameters.setNodeWeightsToTotalEdgeWeights, parameters.firstNeighborIndices, parameters.neighbors, parameters.edgeWeights, parameters.checkIntegrity) + } else if (parameters.nNodes && parameters.edges) { + this.initializeNetworkBasedOnEdges(parameters.nNodes, parameters.nodeWeights, parameters.setNodeWeightsToTotalEdgeWeights, parameters.edges, parameters.edgeWeights, parameters.sortedEdges, parameters.checkIntegrity) + } else if (parameters.nNodes && parameters.firstNeighborIndices && parameters.neighbors) { + this.initializeNetworkBasedOnNeighbors(parameters.nNodes, parameters.nodeWeights, parameters.setNodeWeightsToTotalEdgeWeights, parameters.firstNeighborIndices, parameters.neighbors, parameters.edgeWeights, parameters.checkIntegrity) + } + } + } + + /** + * Returns the number of nodes. + * + * @return Number of nodes + */ + public getNNodes (): number { + return this.nNodes + } + + /** + * Returns the total node weight. + * + * @return Total node weight + */ + public getTotalNodeWeight (): number { + return calcSum(this.nodeWeights) + } + + /** + * Returns the weight of each node. + * + * @return Weight of each node + */ + public getNodeWeights (): number[] { + return this.nodeWeights.slice() + } + + /** + * Returns the weight of a node. + * + * @param node Node + * + * @return Weight + */ + public getNodeWeight (node: number): number { + return this.nodeWeights[node] + } + + /** + * Returns the number of edges. + * + * Each edge is counted only once, even though an edge runs in two + * directions. This means that the number of edges returned by + * {@link getEdges} equals twice the number of edges returned by + * {@link getNEdges}. + * + * @return Number of edges + */ + public getNEdges (): number { + return this.nEdges / 2 + } + + /** + * Returns the number of neighbors per node. + * + * @return Number of neighbors per node + */ + public getNNeighborsPerNode (): number[] { + const nNeighborsPerNode = new Array(this.nNodes) + for (let i = 0; i < this.nNodes; i++) { + nNeighborsPerNode[i] = this.firstNeighborIndices[i + 1] - this.firstNeighborIndices[i] + } + return nNeighborsPerNode + } + + /** + * Returns the number of neighbors of a node. + * + * @param node Node + * + * @return Number of neighbors + */ + public getNNeighbors (node: number): number { + return this.firstNeighborIndices[node + 1] - this.firstNeighborIndices[node] + } + + /** + * Returns the list of edges. + * + * Each edge is included twice, once in each direction. This means that the + * number of edges returned by {@link getEdges} equals twice the number of + * edges returned by {@link getNEdges}. + * + * The list of edges is returned in a two-dimensional array `edges`. + * Edge `i` connects nodes `edges[0][i]` and `edges[1][i]`. + * + * @return List of edges + */ + public getEdges (): number[][] { + const edges = new Array>(2) + edges[0] = new Array(this.nEdges) + for (let i = 0; i < this.nNodes; i++) { + edges[0].fill(i, this.firstNeighborIndices[i], this.firstNeighborIndices[i + 1]) + } + edges[1] = this.neighbors.slice() + return edges + } + + /** + * Returns a list of neighbors per node. + * + * @return List of neighbors per node + */ + public getNeighborsPerNode (): number[][] { + const neighborsPerNode = new Array>(this.nNodes) + for (let i = 0; i < this.nNodes; i++) { + neighborsPerNode[i] = this.neighbors.slice(this.firstNeighborIndices[i], this.firstNeighborIndices[i + 1]) + } + return neighborsPerNode + } + + /** + * Returns the list of neighbors of a node. + * + * @param node Node + * + * @return List of neighbors + */ + public getNeighbors (node: number): number[] { + return this.neighbors.slice(this.firstNeighborIndices[node], this.firstNeighborIndices[node + 1]) + } + + /** + * Returns the total edge weight per node. The total edge weight of a node + * equals the sum of the weights of the edges between the node and its + * neighbors. + * + * @return Total edge weight per node + */ + public getTotalEdgeWeightPerNode (): number[] { + return this.getTotalEdgeWeightPerNodeHelper() + } + + /** + * Returns the total edge weight. + * + * Each edge is considered only once, even though an edge runs in two + * directions. This means that the sum of the edge weights returned by + * {@link getEdgeWeights} equals twice the total edge weight returned by + * {@link getTotalEdgeWeight}. + * + * Edge weights of self links are not included. + * + * @param node Node + * + * @return Total edge weight + */ + public getTotalEdgeWeight (node?: number): number { + return node === undefined ? calcSum(this.edgeWeights) / 2 : calcSumWithinRange(this.edgeWeights, this.firstNeighborIndices[node], this.firstNeighborIndices[node + 1]) + } + + /** + * Returns a list of edge weights per node. These are the weights of the + * edges between a node and its neighbors. + * + * @return List of edge weights per node + */ + public getEdgeWeightsPerNode (): number[][] { + const edgeWeightsPerNode = new Array>(this.nNodes) + for (let i = 0; i < this.nNodes; i++) { + edgeWeightsPerNode[i] = this.edgeWeights.slice(this.firstNeighborIndices[i], this.firstNeighborIndices[i + 1]) + } + return edgeWeightsPerNode + } + + /** + * Returns the list of edge weights of a node. These are the weights of the + * edges between the node and its neighbors. + * + * @param node Node + * + * @return List of edge weights + */ + public getEdgeWeights (node?: number): number[] { + return node === undefined ? this.edgeWeights.slice() : this.edgeWeights.slice(this.firstNeighborIndices[node], this.firstNeighborIndices[node + 1]) + } + + /** + * Returns the total edge weight of self links. + * + * @return Total edge weight of self links + */ + public getTotalEdgeWeightSelfLinks (): number { + return this.totalEdgeWeightSelfLinks + } + + /** + * Creates a copy of the network, but without node weights. + * + * Each node is assigned a weight of 1. + * + * @return Network without node weights + */ + public createNetworkWithoutNodeWeights (): Network { + const networkWithoutNodeWeights = new Network() + networkWithoutNodeWeights.nNodes = this.nNodes + networkWithoutNodeWeights.nEdges = this.nEdges + networkWithoutNodeWeights.nodeWeights = new Array(this.nNodes).fill(1) + networkWithoutNodeWeights.firstNeighborIndices = this.firstNeighborIndices + networkWithoutNodeWeights.neighbors = this.neighbors + networkWithoutNodeWeights.edgeWeights = this.edgeWeights + networkWithoutNodeWeights.totalEdgeWeightSelfLinks = this.totalEdgeWeightSelfLinks + return networkWithoutNodeWeights + } + + /** + * Creates a copy of the network, but without edge weights. + * + * Each edge is assigned a weight of 1. + * + * @return Network without edge weights + */ + public createNetworkWithoutEdgeWeights (): Network { + const networkWithoutEdgeWeights = new Network() + networkWithoutEdgeWeights.nNodes = this.nNodes + networkWithoutEdgeWeights.nEdges = this.nEdges + networkWithoutEdgeWeights.nodeWeights = this.nodeWeights + networkWithoutEdgeWeights.firstNeighborIndices = this.firstNeighborIndices + networkWithoutEdgeWeights.neighbors = this.neighbors + networkWithoutEdgeWeights.edgeWeights = new Array(this.nEdges).fill(1) + networkWithoutEdgeWeights.totalEdgeWeightSelfLinks = 0 + return networkWithoutEdgeWeights + } + + /** + * Creates a copy of the network, but without node and edge weights. + * + * Each node is assigned a weight of 1, and each edge is assigned a weight + * of 1. + * + * @return Network without node and edge weights + */ + public createNetworkWithoutNodeAndEdgeWeights (): Network { + const networkWithoutNodeAndEdgeWeights = new Network() + networkWithoutNodeAndEdgeWeights.nNodes = this.nNodes + networkWithoutNodeAndEdgeWeights.nEdges = this.nEdges + networkWithoutNodeAndEdgeWeights.nodeWeights = new Array(this.nNodes).fill(1) + networkWithoutNodeAndEdgeWeights.firstNeighborIndices = this.firstNeighborIndices + networkWithoutNodeAndEdgeWeights.neighbors = this.neighbors + networkWithoutNodeAndEdgeWeights.edgeWeights = new Array(this.nEdges).fill(1) + networkWithoutNodeAndEdgeWeights.totalEdgeWeightSelfLinks = 0 + return networkWithoutNodeAndEdgeWeights + } + + /** + * Creates a copy of the network in which the edge weights have been + * normalized using the association strength. + * + * The normalized weight `a'[i][j]` of the edge between nodes `i` and `j` is + * given by + * + * ``` + * a'[i][j] = a[i][j] / (n[i] * n[j] / (2 * m)), + * ``` + * + * where `a[i][j]` is the non-normalized weight of the edge between nodes `i` + * and `j`, `n[i]` is the weight of node `i`, and `m` is half the total node + * weight. + * + * If each node's weight equals the total weight of the edges between the + * node and its neighbors, the edge weights are normalized by dividing them + * by the expected edge weights in the random configuration model. + * + * The node weights are set to 1. + * + * @return Normalized network + */ + public createNormalizedNetworkUsingAssociationStrength (): Network { + const normalizedNetwork = new Network() + + normalizedNetwork.nNodes = this.nNodes + normalizedNetwork.nEdges = this.nEdges + normalizedNetwork.nodeWeights = new Array(this.nNodes).fill(1) + normalizedNetwork.firstNeighborIndices = this.firstNeighborIndices + normalizedNetwork.neighbors = this.neighbors + + normalizedNetwork.edgeWeights = new Array(this.nEdges) + const totalNodeWeight = this.getTotalNodeWeight() + for (let i = 0; i < this.nNodes; i++) { + for (let j = this.firstNeighborIndices[i]; j < this.firstNeighborIndices[i + 1]; j++) { + normalizedNetwork.edgeWeights[j] = this.edgeWeights[j] / ((this.nodeWeights[i] * this.nodeWeights[this.neighbors[j]]) / totalNodeWeight) + } + } + + normalizedNetwork.totalEdgeWeightSelfLinks = 0 + + return normalizedNetwork + } + + /** + * Creates a copy of the network in which the edge weights have been + * normalized using fractionalization. + * + * The normalized weight `a'[i][j]` of the edge between nodes `i` and `j` is + * given by + * + * ``` + * a'[i][j] = a[i][j] * (n / n[i] + n / n[j]) / 2, + * ``` + * + * where `a[i][j]` is the non-normalized weight of the edge between nodes `i` + * and `j`, `n[i]` is the weight of node `i`, and `n` is the number of nodes. + * + * The node weights are set to 1. + * + * @return Normalized network + */ + public createNormalizedNetworkUsingFractionalization (): Network { + const normalizedNetwork = new Network() + + normalizedNetwork.nNodes = this.nNodes + normalizedNetwork.nEdges = this.nEdges + normalizedNetwork.nodeWeights = new Array(this.nNodes).fill(1) + normalizedNetwork.firstNeighborIndices = this.firstNeighborIndices + normalizedNetwork.neighbors = this.neighbors + + normalizedNetwork.edgeWeights = new Array(this.nEdges) + for (let i = 0; i < this.nNodes; i++) { + for (let j = this.firstNeighborIndices[i]; j < this.firstNeighborIndices[i + 1]; j++) { + normalizedNetwork.edgeWeights[j] = this.edgeWeights[j] / (2 / (this.nNodes / this.nodeWeights[i] + this.nNodes / this.nodeWeights[this.neighbors[j]])) + } + } + + normalizedNetwork.totalEdgeWeightSelfLinks = 0 + + return normalizedNetwork + } + + /** + * Creates a copy of the network that has been pruned in order to have a + * specified maximum number of edges. + * + * Only the edges with the highest weights are retained in the pruned + * network. In case of ties, the edges to be retained are selected + * randomly. + * + * @param maxNEdges Maximum number of edges + * @param random Random number generator + * + * @return Pruned network + */ + public createPrunedNetwork (maxNEdges: number, random: Random = new Random()): Network { + maxNEdges *= 2 + + if (maxNEdges >= this.nEdges) return this + + const edgeWeights = new Array(this.nEdges / 2).fill(0) + let i = 0 + for (let j = 0; j < this.nNodes; j++) { + let k = this.firstNeighborIndices[j] + while ((k < this.firstNeighborIndices[j + 1]) && (this.neighbors[k] < j)) { + edgeWeights[i] = this.edgeWeights[k] + i++ + k++ + } + } + edgeWeights.sort((a, b) => a - b) + const edgeWeightThreshold = edgeWeights[(this.nEdges - maxNEdges) / 2] + + let nEdgesAboveThreshold = 0 + while (edgeWeights[this.nEdges / 2 - nEdgesAboveThreshold - 1] > edgeWeightThreshold) { + nEdgesAboveThreshold++ + } + let nEdgesAtThreshold = 0 + while ((nEdgesAboveThreshold + nEdgesAtThreshold < this.nEdges / 2) && (edgeWeights[this.nEdges / 2 - nEdgesAboveThreshold - nEdgesAtThreshold - 1] === edgeWeightThreshold)) { + nEdgesAtThreshold++ + } + + const randomNumbers = createDoubleArrayOfRandomNumbers(this.nNodes * this.nNodes, random) + + const randomNumbersEdgesAtThreshold = new Array(nEdgesAtThreshold).fill(0) + i = 0 + for (let j = 0; j < this.nNodes; j++) { + let k = this.firstNeighborIndices[j] + while ((k < this.firstNeighborIndices[j + 1]) && (this.neighbors[k] < j)) { + if (this.edgeWeights[k] === edgeWeightThreshold) { + randomNumbersEdgesAtThreshold[i] = this.getRandomNumber(j, this.neighbors[k], randomNumbers) + i++ + } + k++ + } + } + randomNumbersEdgesAtThreshold.sort((a, b) => a - b) + const randomNumberThreshold = randomNumbersEdgesAtThreshold[nEdgesAboveThreshold + nEdgesAtThreshold - maxNEdges / 2] + + const prunedNetwork = new Network() + + prunedNetwork.nNodes = this.nNodes + prunedNetwork.nEdges = maxNEdges + prunedNetwork.nodeWeights = this.nodeWeights + + prunedNetwork.firstNeighborIndices = new Array(this.nNodes + 1).fill(0) + prunedNetwork.neighbors = new Array(maxNEdges).fill(0) + prunedNetwork.edgeWeights = new Array(maxNEdges).fill(0) + i = 0 + for (let j = 0; j < this.nNodes; j++) { + for (let k = this.firstNeighborIndices[j]; k < this.firstNeighborIndices[j + 1]; k++) { + if ((this.edgeWeights[k] > edgeWeightThreshold) || ((this.edgeWeights[k] === edgeWeightThreshold) && (this.getRandomNumber(j, this.neighbors[k], randomNumbers) >= randomNumberThreshold))) { + prunedNetwork.neighbors[i] = this.neighbors[k] + prunedNetwork.edgeWeights[i] = this.edgeWeights[k] + i++ + } + } + prunedNetwork.firstNeighborIndices[j + 1] = i + } + + prunedNetwork.totalEdgeWeightSelfLinks = 0 + + return prunedNetwork + } + + /** + * Creates an induced subnetwork for specified nodes. + * + * @param nodes Nodes + * + * @return Subnetwork + */ + public createSubnetworkForNodes1 (nodes: number[]): Network { + const subnetwork = new Network() + + subnetwork.nNodes = nodes.length + + if (subnetwork.nNodes === 1) { + subnetwork.nEdges = 0 + subnetwork.nodeWeights = new Array(1) + subnetwork.nodeWeights[0] = this.nodeWeights[nodes[0]] + subnetwork.firstNeighborIndices = new Array(2).fill(0) + subnetwork.neighbors = new Array(0) + subnetwork.edgeWeights = new Array(0) + } else { + const subnetworkNodes = new Array(this.nNodes).fill(-1) + for (let i = 0; i < nodes.length; i++) { + subnetworkNodes[nodes[i]] = i + } + + subnetwork.nEdges = 0 + subnetwork.nodeWeights = new Array(subnetwork.nNodes) + subnetwork.firstNeighborIndices = new Array(subnetwork.nNodes + 1).fill(0) + const subnetworkNeighbors = new Array(this.nEdges).fill(0) + const subnetworkEdgeWeights = new Array(this.nEdges).fill(0) + for (let i = 0; i < subnetwork.nNodes; i++) { + const j = nodes[i] + subnetwork.nodeWeights[i] = this.nodeWeights[j] + for (let k = this.firstNeighborIndices[j]; k < this.firstNeighborIndices[j + 1]; k++) { + if (subnetworkNodes[this.neighbors[k]] >= 0) { + subnetworkNeighbors[subnetwork.nEdges] = subnetworkNodes[this.neighbors[k]] + subnetworkEdgeWeights[subnetwork.nEdges] = this.edgeWeights[k] + subnetwork.nEdges++ + } + } + + subnetwork.firstNeighborIndices[i + 1] = subnetwork.nEdges + } + subnetwork.neighbors = subnetworkNeighbors.slice(0, subnetwork.nEdges) + subnetwork.edgeWeights = subnetworkEdgeWeights.slice(0, subnetwork.nEdges) + } + + subnetwork.totalEdgeWeightSelfLinks = 0 + + return subnetwork + } + + /** + * Creates an induced subnetwork for specified nodes. + * + * @param nodesInSubnetwork Indicates the nodes to be included in the + * subnetwork. + * + * @return Subnetwork + */ + public createSubnetworkForNodes2 (nodesInSubnetwork: boolean[]): Network { + let i = 0 + for (let j = 0; j < this.nNodes; j++) { + if (nodesInSubnetwork[j]) { + i++ + } + } + const nodes = new Array(i).fill(0) + i = 0 + for (let j = 0; j < this.nNodes; j++) { + if (nodesInSubnetwork[j]) { + nodes[i] = j + i++ + } + } + return this.createSubnetworkForNodes1(nodes) + } + + /** + * Creates an induced subnetwork for a specified cluster in a clustering. + * + * If subnetworks need to be created for all clusters in a clustering, it + * is more efficient to use {@link createSubnetworks}. + * + * @param clustering Clustering + * @param cluster Cluster + * + * @return Subnetwork + */ + public createSubnetworkForCluster (clustering: Clustering, cluster: number): Network { + const nodesPerCluster = clustering.getNodesPerCluster() + const subnetworkNodes = new Array(this.nNodes).fill(0) + const subnetworkNeighbors = new Array(this.nEdges).fill(0) + const subnetworkEdgeWeights = new Array(this.nEdges).fill(0) + return this.createSubnetwork(clustering, cluster, nodesPerCluster[cluster], subnetworkNodes, subnetworkNeighbors, subnetworkEdgeWeights) + } + + /** + * Creates induced subnetworks for the clusters in a clustering. + * + * @param clustering Clustering + * + * @return Subnetworks + */ + public createSubnetworks (clustering: Clustering): Network[] { + const subnetworks = new Array(clustering.nClusters) + const nodesPerCluster = clustering.getNodesPerCluster() + const subnetworkNodes = new Array(this.nNodes).fill(0) + const subnetworkNeighbors = new Array(this.nEdges).fill(0) + const subnetworkEdgeWeights = new Array(this.nEdges).fill(0) + for (let i = 0; i < clustering.nClusters; i++) { + subnetworks[i] = this.createSubnetwork(clustering, i, nodesPerCluster[i], subnetworkNodes, subnetworkNeighbors, subnetworkEdgeWeights) + } + return subnetworks + } + + /** + * Creates an induced subnetwork of the largest connected component. + * + * @return Subnetwork + */ + public createSubnetworkLargestComponent (): Network { + return this.createSubnetworkForCluster(this.identifyComponents(), 0) + } + + /** + * Creates a reduced (or aggregate) network based on a clustering. + * + * Each node in the reduced network corresponds to a cluster of nodes in + * the original network. The weight of a node in the reduced network equals + * the sum of the weights of the nodes in the corresponding cluster in the + * original network. The weight of an edge between two nodes in the reduced + * network equals the sum of the weights of the edges between the nodes in + * the two corresponding clusters in the original network. + * + * @param clustering Clustering + * + * @return Reduced network + */ + public createReducedNetwork (clustering: Clustering): Network { + const reducedNetwork = new Network() + + reducedNetwork.nNodes = clustering.nClusters + + reducedNetwork.nEdges = 0 + reducedNetwork.nodeWeights = new Array(clustering.nClusters).fill(0) + reducedNetwork.firstNeighborIndices = new Array(clustering.nClusters + 1).fill(0) + reducedNetwork.totalEdgeWeightSelfLinks = this.totalEdgeWeightSelfLinks + const reducedNetworkNeighbors1 = new Array(this.nEdges).fill(0) + const reducedNetworkEdgeWeights1 = new Array(this.nEdges).fill(0) + const reducedNetworkNeighbors2 = new Array(clustering.nClusters - 1).fill(0) + const reducedNetworkEdgeWeights2 = new Array(clustering.nClusters).fill(0) + const nodesPerCluster = clustering.getNodesPerCluster() + for (let i = 0; i < clustering.nClusters; i++) { + let j = 0 + for (let k = 0; k < nodesPerCluster[i].length; k++) { + const l = nodesPerCluster[i][k] + + reducedNetwork.nodeWeights[i] += this.nodeWeights[l] + + for (let m = this.firstNeighborIndices[l]; m < this.firstNeighborIndices[l + 1]; m++) { + const n = clustering.clusters[this.neighbors[m]] + if (n !== i) { + if (reducedNetworkEdgeWeights2[n] === 0) { + reducedNetworkNeighbors2[j] = n + j++ + } + reducedNetworkEdgeWeights2[n] += this.edgeWeights[m] + } else { + reducedNetwork.totalEdgeWeightSelfLinks += this.edgeWeights[m] + } + } + } + + for (let k = 0; k < j; k++) { + reducedNetworkNeighbors1[reducedNetwork.nEdges + k] = reducedNetworkNeighbors2[k] + reducedNetworkEdgeWeights1[reducedNetwork.nEdges + k] = reducedNetworkEdgeWeights2[reducedNetworkNeighbors2[k]] + reducedNetworkEdgeWeights2[reducedNetworkNeighbors2[k]] = 0 + } + reducedNetwork.nEdges += j + reducedNetwork.firstNeighborIndices[i + 1] = reducedNetwork.nEdges + } + reducedNetwork.neighbors = reducedNetworkNeighbors1.slice(0, reducedNetwork.nEdges) + reducedNetwork.edgeWeights = reducedNetworkEdgeWeights1.slice(0, reducedNetwork.nEdges) + + return reducedNetwork + } + + /** + * Identifies the connected components of the network. + * + * @return Connected components + */ + public identifyComponents (): Clustering { + const componentsAlgorithm = new ComponentsAlgorithm() + return componentsAlgorithm.findClustering(this) + } + + /** + * Checks the integrity of the network. + * + * It is checked whether: + * + *
    + *
  • variables have a correct value,
    • + *
  • arrays have a correct length,
    • + *
  • edges are sorted correctly,
    • + *
  • edges are stored in both directions.
    • + *
+ * + * An exception is thrown if the integrity of the network is violated. + * + * @throws An illegal argument was provided in the construction of the network. + */ + private checkIntegrity (): void { + // Check whether variables have a correct value and arrays have a correct length. + if (this.nNodes < 0) { + throw new Error('nNodes must be non-negative.') + } + if (this.nEdges < 0) { + throw new Error('nEdges must be non-negative.') + } + if (this.nEdges % 2 === 1) { + throw new Error('nEdges must be even.') + } + if (this.nodeWeights.length !== this.nNodes) { + throw new Error('Length of nodeWeight array must be equal to nNodes.') + } + if (this.firstNeighborIndices.length !== this.nNodes + 1) { + throw new Error('Length of firstNeighborIndices array must be equal to nNodes + 1.') + } + if (this.firstNeighborIndices[0] !== 0) { + throw new Error('First element of firstNeighborIndices array must be equal to 0.') + } + if (this.firstNeighborIndices[this.nNodes] !== this.nEdges) { + throw new Error('Last element of firstNeighborIndices array must be equal to nEdges.') + } + if (this.neighbors.length !== this.nEdges) { + throw new Error('Length of neighbors array must be equal to nEdges.') + } + if (this.edgeWeights.length !== this.nEdges) { + throw new Error('Length of edgeWeights array must be equal to nEdges.') + } + + // Check whether edges are sorted correctly. + for (let i = 0; i < this.nNodes; i++) { + if (this.firstNeighborIndices[i + 1] < this.firstNeighborIndices[i]) { + throw new Error('Elements of firstNeighborIndices array must be in non-decreasing order.') + } + for (let j = this.firstNeighborIndices[i]; j < this.firstNeighborIndices[i + 1]; j++) { + const k = this.neighbors[j] + if (k < 0) { + throw new Error('Elements of neighbors array must have non-negative values.') + } else if (k >= this.nNodes) { + throw new Error('Elements of neighbors array must have values less than nNodes.') + } + if (j > this.firstNeighborIndices[i]) { + const l = this.neighbors[j - 1] + if (k < l) { + throw new Error('For each node, corresponding elements of neighbors array must be in increasing order.') + } else if (k === l) { + throw new Error('For each node, corresponding elements of neighbors array must not include duplicate values.') + } + } + } + } + + // Check whether edges are stored in both directions. + const checked = new Array(this.nEdges) + for (let i = 0; i < this.nNodes; i++) { + for (let j = this.firstNeighborIndices[i]; j < this.firstNeighborIndices[i + 1]; j++) { + if (!checked[j]) { + const k = this.neighbors[j] + const l = binarySearch(this.neighbors, this.firstNeighborIndices[k], this.firstNeighborIndices[k + 1], i) + if (l < 0) { + throw new Error('Edges must be stored in both directions.') + } + if (this.edgeWeights[j] !== this.edgeWeights[l]) { + throw new Error('Edge weights must be the same in both directions.') + } + checked[j] = true + checked[l] = true + } + } + } + } + + private initializeNetworkBasedOnEdges (nNodes: number, nodeWeights: number[] | undefined, setNodeWeightsToTotalEdgeWeights: boolean | undefined, edges: number[][], edgeWeights: number[] | undefined, sortedEdges: boolean | undefined, checkIntegrity: boolean | undefined): void { + let i: number + if (!sortedEdges) { + const edges2 = [new Array(2 * edges[0].length), new Array(2 * edges[0].length)] + const edgeWeights2 = edgeWeights !== undefined ? new Array(2 * edges[0].length) : undefined + + i = 0 + for (let j = 0; j < edges[0].length; j++) { + edges2[0][i] = edges[0][j] + edges2[1][i] = edges[1][j] + + if (edgeWeights !== undefined && edgeWeights2 !== undefined) edgeWeights2[i] = edgeWeights[j] + i++ + if (edges[0][j] !== edges[1][j]) { + edges2[0][i] = edges[1][j] + edges2[1][i] = edges[0][j] + if (edgeWeights !== undefined && edgeWeights2 !== undefined) edgeWeights2[i] = edgeWeights[j] + i++ + } + } + edges[0] = edges2[0].slice(0, i) + edges[1] = edges2[1].slice(0, i) + if (edgeWeights !== undefined && edgeWeights2 !== undefined) edgeWeights = edgeWeights2.slice(0, i) + this.sortEdges(edges, edgeWeights) + } + this.nNodes = nNodes + this.nEdges = 0 + this.firstNeighborIndices = new Array(nNodes + 1).fill(0) + this.neighbors = new Array(edges[0].length).fill(0) + this.edgeWeights = new Array(edges[0].length).fill(0) + this.totalEdgeWeightSelfLinks = 0 + i = 1 + for (let j = 0; j < edges[0].length; j++) { + if (edges[0][j] !== edges[1][j]) { + for (; i <= edges[0][j]; i++) { + this.firstNeighborIndices[i] = this.nEdges + } + this.neighbors[this.nEdges] = edges[1][j] + this.edgeWeights[this.nEdges] = edgeWeights !== undefined ? edgeWeights[j] : 1 + this.nEdges++ + } else { + this.totalEdgeWeightSelfLinks += edgeWeights !== undefined ? edgeWeights[j] : 1 + } + } + + for (; i <= nNodes; i++) { + this.firstNeighborIndices[i] = this.nEdges + } + this.neighbors = this.neighbors.slice(0, this.nEdges) + this.edgeWeights = this.edgeWeights.slice(0, this.nEdges) + + if (typeof nodeWeights !== 'undefined') { + this.nodeWeights = nodeWeights.slice() + } else { + this.nodeWeights = setNodeWeightsToTotalEdgeWeights ? this.getTotalEdgeWeightPerNodeHelper() : new Array(this.nNodes).fill(1) + } + + if (checkIntegrity) this.checkIntegrity() + } + + private initializeNetworkBasedOnNeighbors (nNodes: number, nodeWeights: number[] | undefined, setNodeWeightsToTotalEdgeWeights: boolean | undefined, firstNeighborIndices: number[], neighbors: number[], edgeWeights: number[] | undefined, checkIntegrity: boolean | undefined): void { + this.nNodes = nNodes + this.nEdges = neighbors.length + this.firstNeighborIndices = firstNeighborIndices.slice() + this.neighbors = neighbors.slice() + this.edgeWeights = edgeWeights ? edgeWeights.slice() : new Array(this.nEdges).fill(1) + this.totalEdgeWeightSelfLinks = 0 + + if (nodeWeights !== undefined) { + this.nodeWeights = nodeWeights.slice() + } else { + this.nodeWeights = setNodeWeightsToTotalEdgeWeights ? this.getTotalEdgeWeightPerNodeHelper() : new Array(this.nNodes).fill(1) + } + + if (checkIntegrity) this.checkIntegrity() + } + + private getTotalEdgeWeightPerNodeHelper (): number[] { + const totalEdgeWeightPerNode = new Array(this.nNodes) + for (let i = 0; i < this.nNodes; i++) { + totalEdgeWeightPerNode[i] = calcSumWithinRange(this.edgeWeights, this.firstNeighborIndices[i], this.firstNeighborIndices[i + 1]) + } + return totalEdgeWeightPerNode + } + + private getRandomNumber (node1: number, node2: number, randomNumbers: number[]): number { + let i: number + let j: number + if (node1 < node2) { + i = node1 + j = node2 + } else { + i = node2 + j = node1 + } + return randomNumbers[i * this.nNodes + j] + } + + private createSubnetwork (clustering: Clustering, cluster: number, nodes: number[], subnetworkNodes: number[], subnetworkNeighbors: number[], subnetworkEdgeWeights: number[]): Network { + const subnetwork = new Network() + + subnetwork.nNodes = nodes.length + + if (subnetwork.nNodes === 1) { + subnetwork.nEdges = 0 + subnetwork.nodeWeights = new Array(1) + subnetwork.nodeWeights[0] = this.nodeWeights[nodes[0]] + subnetwork.firstNeighborIndices = new Array(2).fill(0) + subnetwork.neighbors = new Array(0) + subnetwork.edgeWeights = new Array(0) + } else { + for (let i = 0; i < nodes.length; i++) { + subnetworkNodes[nodes[i]] = i + } + + subnetwork.nEdges = 0 + subnetwork.nodeWeights = new Array(subnetwork.nNodes) + subnetwork.firstNeighborIndices = new Array(subnetwork.nNodes + 1).fill(0) + for (let i = 0; i < subnetwork.nNodes; i++) { + const j = nodes[i] + subnetwork.nodeWeights[i] = this.nodeWeights[j] + for (let k = this.firstNeighborIndices[j]; k < this.firstNeighborIndices[j + 1]; k++) { + if (clustering.clusters[this.neighbors[k]] === cluster) { + subnetworkNeighbors[subnetwork.nEdges] = subnetworkNodes[this.neighbors[k]] + subnetworkEdgeWeights[subnetwork.nEdges] = this.edgeWeights[k] + subnetwork.nEdges++ + } + } + subnetwork.firstNeighborIndices[i + 1] = subnetwork.nEdges + } + subnetwork.neighbors = subnetworkNeighbors.slice(0, subnetwork.nEdges) + subnetwork.edgeWeights = subnetworkEdgeWeights.slice(0, subnetwork.nEdges) + } + + subnetwork.totalEdgeWeightSelfLinks = 0 + + return subnetwork + } + + private sortEdges (edges: number[][], edgeWeights?: number[]): void { + function compareEdges (edges: number[][], i: number, j: number): number { + if (edges[0][i] > edges[0][j]) return 1 + if (edges[0][i] < edges[0][j]) return -1 + if (edges[1][i] > edges[1][j]) return 1 + if (edges[1][i] < edges[1][j]) return -1 + return 0 + } + + const nEdges = edges[0].length + + // Determine sorting order. + const indices = [...Array(nEdges).keys()] + indices.sort((a, b) => compareEdges(edges, a, b)) + + // Sort edges. + const edgesSorted = new Array>(2) + edgesSorted[0] = new Array(nEdges) + edgesSorted[1] = new Array(nEdges) + for (let i = 0; i < nEdges; i++) { + edgesSorted[0][i] = edges[0][indices[i]] + edgesSorted[1][i] = edges[1][indices[i]] + } + edges[0] = edgesSorted[0] + edges[1] = edgesSorted[1] + + // Sort edge weights. + if (edgeWeights !== undefined) { + const edgeWeightsSorted = new Array(nEdges) + for (let i = 0; i < nEdges; i++) { + edgeWeightsSorted[i] = edgeWeights[indices[i]] + } + Object.assign(edgeWeights, edgeWeightsSorted) + } + } +} diff --git a/src/qualityClusteringAlgorithm.ts b/src/qualityClusteringAlgorithm.ts new file mode 100644 index 0000000..c8493a2 --- /dev/null +++ b/src/qualityClusteringAlgorithm.ts @@ -0,0 +1,18 @@ +import Clustering from './clustering' +import ClusteringAlgorithm from './clusteringAlgorithm' +import Network from './network' + +/** + * Interface for clustering algorithms that use a quality function. + */ +export default interface QualityClusteringAlgorithm extends ClusteringAlgorithm { + /** + * Calculates the quality of a clustering of the nodes in a network. + * + * @param network Network + * @param clustering Clustering + * + * @return Quality of the clustering + */ + calcQuality (network: Network, clustering: Clustering): number +} diff --git a/src/qualityLayoutAlgorithm.ts b/src/qualityLayoutAlgorithm.ts new file mode 100644 index 0000000..d6def22 --- /dev/null +++ b/src/qualityLayoutAlgorithm.ts @@ -0,0 +1,18 @@ +import Layout from './layout' +import LayoutAlgorithm from './layoutAlgorithm' +import Network from './network' + +/** + * Interface for layout algorithms that use a quality function. + */ +export default interface QualityLayoutAlgorithm extends LayoutAlgorithm { + /** + * Calculates the quality of a layout of the nodes in a network. + * + * @param network Network + * @param layout Layout + * + * @return Quality of the layout + */ + calcQuality (network: Network, layout: Layout): number +} diff --git a/src/run/enums.ts b/src/run/enums.ts new file mode 100644 index 0000000..dd101a0 --- /dev/null +++ b/src/run/enums.ts @@ -0,0 +1,32 @@ +/** + * Clustering quality functions. + */ +export enum ClusteringQualityFunctions { + CPM = 'CPM', + Modularity = 'Modularity', +} + +/** + * Clustering algorithms. + */ +export enum ClusteringAlgorithms { + Leiden = 'Leiden', + Louvain = 'Louvain', +} + +/** + * Layout quality functions. + */ +export enum LayoutQualityFunctions { + VOS = 'VOS', + LinLog = 'LinLog', +} + +/** + * Normalization methods. + */ +export enum NormalizationMethods { + NoNormalization = 'No normalization', + AssociationStrength = 'Association strength', + Fractionalization = 'Fractionalization', +} diff --git a/src/run/index.ts b/src/run/index.ts new file mode 100644 index 0000000..cd76b1f --- /dev/null +++ b/src/run/index.ts @@ -0,0 +1,27 @@ +/** + * Provides classes and types for running network analysis algorithms. + * + * @module + */ +import NetworkAnalysis from './networkAnalysis' +import NetworkClustering from './networkClustering' +import NetworkLayout from './networkLayout' +import NetworkHelper from './networkHelper' +import { ClusteringQualityFunctions, ClusteringAlgorithms, LayoutQualityFunctions, NormalizationMethods } from './enums' +import type { Node, Link } from './types' + +export { + NetworkAnalysis, + NetworkClustering, + ClusteringQualityFunctions, + ClusteringAlgorithms, + NetworkLayout, + LayoutQualityFunctions, + NetworkHelper, + NormalizationMethods, +} + +export type { + Node, + Link, +} diff --git a/src/run/networkAnalysis.ts b/src/run/networkAnalysis.ts new file mode 100644 index 0000000..851377d --- /dev/null +++ b/src/run/networkAnalysis.ts @@ -0,0 +1,19 @@ +import NetworkHelper from './networkHelper' +import type { Node, Link } from './types' + +export default abstract class NetworkAnalysis { + protected _networkHelper: NetworkHelper | undefined + + /** + * Initialize network data. + */ + public data (nodes: Node[], links: Link[]): this { + this._networkHelper = new NetworkHelper(nodes, links) + return this + } + + /** + * Run network analysis. + */ + public abstract run (): void +} diff --git a/src/run/networkClustering.ts b/src/run/networkClustering.ts new file mode 100644 index 0000000..4a88c1c --- /dev/null +++ b/src/run/networkClustering.ts @@ -0,0 +1,185 @@ +/* eslint-disable no-console */ +import Random from 'java-random' +import NetworkAnalysis from './networkAnalysis' +import { ClusteringQualityFunctions, ClusteringAlgorithms, NormalizationMethods } from './enums' +import CPMClusteringAlgorithm from '../CPMClusteringAlgorithm' +import IterativeCPMClusteringAlgorithm from '../iterativeCPMClusteringAlgorithm' +import LouvainAlgorithm from '../louvainAlgorithm' +import LeidenAlgorithm from '../leidenAlgorithm' +import Clustering from '../clustering' + +/** + * Class for running the Leiden and Louvain algorithms for network clustering. + */ +export default class NetworkClustering extends NetworkAnalysis { + private _useModularity = false + private _normalization: NormalizationMethods = NormalizationMethods.NoNormalization + private _resolution = CPMClusteringAlgorithm.DEFAULT_RESOLUTION + private _minClusterSize = 1 + private _useLouvain = false + private _nRandomStarts = 1 + private _nIterations = 10 + private _randomness = LeidenAlgorithm.DEFAULT_RANDOMNESS + private _useSeed = false + private _seed = 0 + + /** + * Quality function to be optimized. Either the CPM (constant Potts model) or + * the modularity quality function can be used. + */ + public qualityFunction (value: keyof typeof ClusteringQualityFunctions): this { + this._useModularity = ClusteringQualityFunctions[value] === ClusteringQualityFunctions.Modularity + return this + } + + /** + * Method for normalizing edge weights in the CPM quality function. + */ + public normalization (value: keyof typeof NormalizationMethods): this { + this._normalization = NormalizationMethods[value] + return this + } + + /** + * Resolution parameter of the quality function. + */ + public resolution (value: number): this { + this._resolution = value + return this + } + + /** + * Minimum number of nodes per cluster. + */ + public minClusterSize (value: number): this { + this._minClusterSize = value + return this + } + + /** + * Algorithm for optimizing the quality function. Either the Leiden or the Louvain algorithm can be used. + */ + public algorithm (value: keyof typeof ClusteringAlgorithms): this { + this._useLouvain = ClusteringAlgorithms[value] === ClusteringAlgorithms.Louvain + return this + } + + /** + * Number of random starts of the algorithm. + */ + public randomStarts (value: number): this { + this._nRandomStarts = value + return this + } + + /** + * Number of random starts of the algorithm. + */ + public iterations (value: number): this { + this._nIterations = value + return this + } + + /** + * Randomness parameter of the Leiden algorithm. + */ + public randomness (value: number): this { + this._randomness = value + return this + } + + /** + * Seed of the random number generator. + */ + public seed (value: number): this { + this._seed = value + this._useSeed = true + return this + } + + /** + * Identify clusters (also known as communities) in a network using either + * the Leiden or the Louvain algorithm. + */ + public run (): void { + if (!this._networkHelper) { + throw new Error('Network data is not initialized.') + } + + let network = this._networkHelper.getNetwork() + console.log(`Network consists of ${network.getNNodes()} nodes and ${network.getNEdges()} edges with a total edge weight of ${network.getTotalEdgeWeight()}.`) + + let initialClustering = this._networkHelper.getInitialClustering() + if (initialClustering !== undefined) { + console.log(`Initial clustering consists of ${initialClustering.getNClusters()} clusters.`) + } else { + initialClustering = new Clustering({ nNodes: network.getNNodes() }) + console.log('Using singleton initial clustering.') + } + + console.log(`Running ${this._useLouvain ? ClusteringAlgorithms.Louvain : ClusteringAlgorithms.Leiden} algorithm.`) + console.log(`Quality function: ${this._useModularity ? ClusteringQualityFunctions.Modularity : ClusteringQualityFunctions.CPM}`) + if (!this._useModularity) console.log(`Normalization method: ${this._normalization}`) + console.log(`Resolution parameter: ${this._resolution}`) + console.log(`Minimum cluster size: ${this._minClusterSize}`) + console.log(`Number of random starts: ${this._nRandomStarts}`) + console.log(`Number of iterations: ${this._nIterations}`) + if (!this._useLouvain) console.log(`Randomness parameter: ${this._randomness}`) + console.log(`Random number generator seed: ${this._useSeed ? this._seed : 'random'}`) + + const startTimeAlgorithm = Date.now() + if (!this._useModularity) { + if (this._normalization === NormalizationMethods.NoNormalization) { + network = network.createNetworkWithoutNodeWeights() + } else if (this._normalization === NormalizationMethods.AssociationStrength) { + network = network.createNormalizedNetworkUsingAssociationStrength() + } else if (this._normalization === NormalizationMethods.Fractionalization) { + network = network.createNormalizedNetworkUsingFractionalization() + } + } + const resolution2 = this._useModularity ? (this._resolution / (2 * network.getTotalEdgeWeight() + network.getTotalEdgeWeightSelfLinks())) : this._resolution + const random = this._useSeed ? new Random(this._seed) : new Random() + let algorithm: IterativeCPMClusteringAlgorithm + if (this._useLouvain) { + const lovainAlgorithm = new LouvainAlgorithm() + lovainAlgorithm.initializeBasedOnResolutionAndNIterationsAndRandom(resolution2, this._nIterations, random) + algorithm = lovainAlgorithm + } else { + const leidenAlgorithm = new LeidenAlgorithm() + leidenAlgorithm.initializeBasedOnResolutionAndNIterationsAndRandomnessAndRandom(resolution2, this._nIterations, this._randomness, random) + algorithm = leidenAlgorithm + } + let finalClustering: Clustering | undefined + let maxQuality = Number.NEGATIVE_INFINITY + for (let i = 0; i < this._nRandomStarts; i++) { + const clustering = initialClustering.clone() + algorithm.improveClustering(network, clustering) + const quality = algorithm.calcQuality(network, clustering) + if (this._nRandomStarts > 1) { + console.log(`Quality function in random start ${i + 1} equals ${quality}.`) + } + if (quality > maxQuality) { + finalClustering = clustering + maxQuality = quality + } + } + if (finalClustering) { + finalClustering.orderClustersByNNodes() + console.log(`Running algorithm took ${(Date.now() - startTimeAlgorithm) / 1000}s.`) + if (this._nRandomStarts > 1) { + console.log(`Maximum value of quality function in ${this._nRandomStarts} random starts equals ${maxQuality}.`) + } else { + console.log(`Quality function equals ${maxQuality}.`) + } + if (this._minClusterSize > 1) { + console.log(`Clustering consists of ${finalClustering.getNClusters()} clusters.`) + console.log(`Removing clusters consisting of fewer than ${this._minClusterSize} nodes.`) + algorithm.removeSmallClustersBasedOnNNodes(network, finalClustering, this._minClusterSize) + } + console.log(`Final clustering consists of ${finalClustering.getNClusters()} clusters.`) + + const clusters = finalClustering.getClusters() + this._networkHelper.setClusters(clusters) + } + } +} diff --git a/src/run/networkHelper.ts b/src/run/networkHelper.ts new file mode 100644 index 0000000..48eda2a --- /dev/null +++ b/src/run/networkHelper.ts @@ -0,0 +1,109 @@ +import Clustering from '../clustering' +import Layout from '../layout' +import Network from '../network' +import type { Node, Link } from './types' + +export default class NetworkHelper { + private _nodes: Node[] + private _network: Network + private _initialClustering: Clustering | undefined + private _initialLayout: Layout | undefined + + public constructor (nodes: Node[], links: Link[]) { + this._nodes = nodes + + // Deduplicate links and convert them to an edge list. + const nodeIdToIndex: { [key: string]: number } = {} + nodes.forEach((node, index) => { + nodeIdToIndex[node.id] = index + }) + const edgeContainer: { [key: string]: { node1Index: number, node2Index: number, weight: number } } = {} + links.forEach(link => { + let node1Index = nodeIdToIndex[link.node1.id] + let node2Index = nodeIdToIndex[link.node2.id] + if (node1Index !== node2Index) { + const weight = link.weight ?? 1 + if (node2Index < node1Index) { + const oldNode1Index = node1Index + node1Index = node2Index + node2Index = oldNode1Index + } + if (edgeContainer[`${node1Index}-${node2Index}`]) { + edgeContainer[`${node1Index}-${node2Index}`].weight += weight + } else { + edgeContainer[`${node1Index}-${node2Index}`] = { node1Index, node2Index, weight } + } + } + }) + const edgeList = Object.values(edgeContainer) + + // Create network object. + let nNodes = this._nodes.length + const nEdges = edgeList.length + const edges = new Array(2) + edges[0] = new Array(nEdges) + edges[1] = new Array(nEdges) + const edgeWeights = new Array(nEdges) + edgeList.forEach((edge, i) => { + edges[0][i] = edge.node1Index + edges[1][i] = edge.node2Index + edgeWeights[i] = edge.weight + if (edge.node1Index > nNodes) nNodes = edge.node1Index + 1 + if (edge.node2Index > nNodes) nNodes = edge.node2Index + 1 + }) + this._network = new Network({ + nNodes: nNodes, + setNodeWeightsToTotalEdgeWeights: true, + edges: edges, + edgeWeights: edgeWeights, + sortedEdges: false, + checkIntegrity: false, + }) + + // Create initial clustering object. + const clusters = nodes.map(node => Number(node.cluster)) + if (!clusters.includes(NaN)) { + const uniqueClusters = [...new Set(clusters)].sort((a, b) => a - b) + const clusterIdToIndex: { [key: number]: number } = {} + uniqueClusters.forEach((cluster, index) => { + clusterIdToIndex[cluster] = index + }) + const zeroIndexedClusters = new Array(clusters.length) + clusters.forEach((cluster, index) => { + zeroIndexedClusters[index] = clusterIdToIndex[cluster] + }) + this._initialClustering = new Clustering({ clusters: zeroIndexedClusters }) + } + + // Create initial layout object. + const coordinates = new Array>(2) + coordinates[0] = nodes.map(item => Number(item.x)) + coordinates[1] = nodes.map(item => Number(item.y)) + if (!coordinates[0].includes(NaN) && !coordinates[1].includes(NaN)) this._initialLayout = new Layout({ coordinates }) + } + + public getNetwork (): Network { + return this._network + } + + public getInitialClustering (): Clustering | undefined { + return this._initialClustering + } + + public getInitialLayout (): Layout | undefined { + return this._initialLayout + } + + public setClusters (clusters: number[]): void { + this._nodes.forEach((node, i) => { + node.cluster = clusters[i] + }) + } + + public setCoordinates (coordinates: number[][]): void { + this._nodes.forEach((node, i) => { + node.x = coordinates[0][i] + node.y = coordinates[1][i] + }) + } +} diff --git a/src/run/networkLayout.ts b/src/run/networkLayout.ts new file mode 100644 index 0000000..786ba16 --- /dev/null +++ b/src/run/networkLayout.ts @@ -0,0 +1,186 @@ +/* eslint-disable no-console */ +import Random from 'java-random' +import NetworkAnalysis from './networkAnalysis' +import { LayoutQualityFunctions, NormalizationMethods } from './enums' +import GradientDescentVOSLayoutAlgorithm from '../gradientDescentVOSLayoutAlgorithm' +import Layout from '../layout' + +/** + * Class for running the gradient descent VOS layout algorithm for network + * layout. + */ +export default class NetworkLayout extends NetworkAnalysis { + private _useLinLog = false + private _normalization: NormalizationMethods = NormalizationMethods.NoNormalization + private _attraction = GradientDescentVOSLayoutAlgorithm.DEFAULT_ATTRACTION + private _repulsion = GradientDescentVOSLayoutAlgorithm.DEFAULT_REPULSION + private _nRandomStarts = 1 + private _maxNIterations = GradientDescentVOSLayoutAlgorithm.DEFAULT_MAX_N_ITERATIONS + private _initialStepSize = GradientDescentVOSLayoutAlgorithm.DEFAULT_INITIAL_STEP_SIZE + private _minStepSize = GradientDescentVOSLayoutAlgorithm.DEFAULT_MIN_STEP_SIZE + private _stepSizeReduction = GradientDescentVOSLayoutAlgorithm.DEFAULT_STEP_SIZE_REDUCTION + private _requiredNQualityValueImprovements = GradientDescentVOSLayoutAlgorithm.DEFAULT_REQUIRED_N_QUALITY_VALUE_IMPROVEMENTS + private _seed = 0 + private _useSeed = false + private _edgeWeightIncrementUnconnectedNodes = 0.01 + + /** + * Quality function to be optimized. Either the VOS (visualization of + * similarities) or the LinLog quality function can be used. + */ + public qualityFunction (value: keyof typeof LayoutQualityFunctions): this { + this._useLinLog = LayoutQualityFunctions[value] === LayoutQualityFunctions.LinLog + return this + } + + /** + * Method for normalizing edge weights in the VOS quality function. + */ + public normalization (value: keyof typeof NormalizationMethods): this { + this._normalization = NormalizationMethods[value] + return this + } + + /** + * Attraction parameter of the VOS quality function. + */ + public attraction (value: number): this { + this._attraction = value + return this + } + + /** + * Maximum number of iterations of the gradient descent algorithm. + */ + public maxNIterations (value: number): this { + this._maxNIterations = value + return this + } + + /** + * Initial step size of the gradient descent algorithm. + */ + public initialStepSize (value: number): this { + this._initialStepSize = value + return this + } + + /** + * Minimum step size of the gradient descent algorithm. + */ + public minStepSize (value: number): this { + this._minStepSize = value + return this + } + + /** + * Step size reduction of the gradient descent algorithm. + */ + public stepSizeReduction (value: number): this { + this._stepSizeReduction = value + return this + } + + /** + * Required number of quality value improvements of the gradient descent algorithm. + */ + public requiredNQualityValueImprovements (value: number): this { + this._requiredNQualityValueImprovements = value + return this + } + + /** + * Repulsion parameter of the VOS quality function. + */ + public repulsion (value: number): this { + this._repulsion = value + return this + } + + /** + * Number of random starts of the gradient descent algorithm. + */ + public randomStarts (value: number): this { + this._nRandomStarts = value + return this + } + + /** + * Seed of the random number generator. + */ + public seed (value: number): this { + this._seed = value + this._useSeed = true + return this + } + + /** + * Determine a layout for a network using the gradient descent VOS layout + * algorithm. + */ + public run (): void { + if (!this._networkHelper) { + throw new Error('Network data is not initialized.') + } + + let network = this._networkHelper.getNetwork() + console.log(`Network consists of ${network.getNNodes()} nodes and ${network.getNEdges()} edges with a total edge weight of ${network.getTotalEdgeWeight()}.`) + + const initialLayout = this._networkHelper.getInitialLayout() + + console.log('Running gradient descent VOS layout algorithm.') + console.log(`Quality function: ${this._useLinLog ? LayoutQualityFunctions.LinLog : LayoutQualityFunctions.VOS}`) + if (!this._useLinLog) console.log(`Normalization method: ${this._normalization}`) + console.log(`Attraction parameter: ${this._attraction}`) + console.log(`Repulsion parameter: ${this._repulsion}`) + console.log(`Number of random starts: ${this._nRandomStarts}`) + console.log(`Maximum number of iterations: ${this._maxNIterations}`) + console.log(`Initial step size: ${this._initialStepSize}`) + console.log(`Minimum step size: ${this._minStepSize}`) + console.log(`Step size reduction: ${this._stepSizeReduction}`) + console.log(`Required number of quality value improvements: ${this._requiredNQualityValueImprovements}`) + console.log(`Random number generator seed: ${this._useSeed ? this._seed : 'random'}`) + + const startTimeAlgorithm = Date.now() + if (!this._useLinLog) { + if (this._normalization === NormalizationMethods.NoNormalization) { + network = network.createNetworkWithoutNodeWeights() + } else if (this._normalization === NormalizationMethods.AssociationStrength) { + network = network.createNormalizedNetworkUsingAssociationStrength() + } else if (this._normalization === NormalizationMethods.Fractionalization) { + network = network.createNormalizedNetworkUsingFractionalization() + } + } + + const edgeWeightIncrement = (network.identifyComponents().getNClusters() > 1) ? this._edgeWeightIncrementUnconnectedNodes : 0 + const random = this._useSeed ? new Random(this._seed) : new Random() + const algorithm = new GradientDescentVOSLayoutAlgorithm() + algorithm.initializeBasedOnAttractionAndRepulsionAndEdgeWeightIncrementAndMaxNIterationsAndInitialStepSizeAndMinStepSizeAndStepSizeReductionAndRequiredNQualityValueImprovementsAndRandom(this._attraction, this._repulsion, edgeWeightIncrement, this._maxNIterations, this._initialStepSize, this._minStepSize, this._stepSizeReduction, this._requiredNQualityValueImprovements, random) + let finalLayout: Layout | undefined + let minQuality = Number.POSITIVE_INFINITY + for (let i = 0; i < this._nRandomStarts; i++) { + const layout = (initialLayout !== undefined) ? initialLayout.clone() : new Layout({ nNodes: network.getNNodes(), random: random }) + algorithm.improveLayout(network, layout) + const quality = algorithm.calcQuality(network, layout) + if (this._nRandomStarts > 1) { + console.log(`Quality function in random start ${i + 1} equals ${quality}.`) + } + if (quality < minQuality) { + finalLayout = layout + minQuality = quality + } + } + if (finalLayout) { + finalLayout.standardize(true) + console.log(`Running algorithm took ${(Date.now() - startTimeAlgorithm) / 1000}s.`) + if (this._nRandomStarts > 1) { + console.log(`Minimum value of quality function in ${this._nRandomStarts} random starts equals ${minQuality}.`) + } else { + console.log(`Quality function equals ${minQuality}.`) + } + + const coordinates = finalLayout.getCoordinates() + this._networkHelper.setCoordinates(coordinates) + } + } +} diff --git a/src/run/types.ts b/src/run/types.ts new file mode 100644 index 0000000..664c7f4 --- /dev/null +++ b/src/run/types.ts @@ -0,0 +1,51 @@ +/** + * A node in a network. + */ +export type Node = { + /** + * ID of a node. + */ + id: string | number + + /** + * Horizontal coordinate of a node. If the coordinates of a node are specified + * before running the layout algorithm, they will be used in the initial + * layout. + */ + x?: number + + /** + * Vertical coordinate of a node. If the coordinates of a node are specified + * before running the layout algorithm, they will be used in the initial + * layout. + */ + y?: number + + /** + * Cluster to which a node belongs. A cluster is represented by a integer. If + * the cluster of a node is specified before running the clustering algorithm, + * it will be used in the initial clustering. + */ + cluster?: number +} + +/** + * A link in a network. + */ +export type Link = { + /** + * ID of a source node. + */ + node1: Node + + /** + * ID of a target node. + */ + node2: Node + + /** + * Weight of the link between a source node and a target node. The weight is a + * non-negative number. + */ + weight?: number +} diff --git a/src/standardLocalMovingAlgorithm.ts b/src/standardLocalMovingAlgorithm.ts new file mode 100644 index 0000000..706e2e3 --- /dev/null +++ b/src/standardLocalMovingAlgorithm.ts @@ -0,0 +1,209 @@ +import Random from 'java-random' +import IncrementalCPMClusteringAlgorithm from './incrementalCPMClusteringAlgorithm' +import Clustering from './clustering' +import Network from './network' +import { generateRandomPermutation } from './utils/arrays' + +/** + * Standard local moving algorithm. + * + * The standard local moving algorithm iterates over the nodes in a network. A + * node is moved to the cluster that results in the largest increase in the + * quality function. If the current cluster assignment of the node is already + * optimal, the node is not moved. The algorithm continues iterating over the + * nodes in a network until no more nodes can be moved. + * + * A fast variant of the standard local moving algorithm is provided by the + * {@link FastLocalMovingAlgorithm}. + */ +export default class StandardLocalMovingAlgorithm extends IncrementalCPMClusteringAlgorithm { + /** + * Random number generator. + */ + protected random!: Random + + /** + * Constructs a standard local moving algorithm. + */ + public constructor () { + super() + this.initializeBasedOnRandom(new Random()) + } + + /** + * Initializes a standard local merging algorithm. + * + * @param random Random number generator + */ + public initializeBasedOnRandom (random: Random): void { + this.initializeBasedOnResolutionAndRandom(StandardLocalMovingAlgorithm.DEFAULT_RESOLUTION, random) + } + + /** + * Initializes a standard local merging algorithm for a specified resolution + * parameter. + * + * @param resolution Resolution parameter + * @param random Random number generator + */ + public initializeBasedOnResolutionAndRandom (resolution: number, random: Random): void { + super.initializeBasedOnResolution(resolution) + this.random = random + } + + /** + * Clones the algorithm. + * + * @return Cloned algorithm + */ + public clone (): StandardLocalMovingAlgorithm { + const clonedAlgorithm = new StandardLocalMovingAlgorithm() + clonedAlgorithm.resolution = this.resolution + clonedAlgorithm.random = this.random + return clonedAlgorithm + } + + /** + * Improves a clustering of the nodes in a network using the standard local + * moving algorithm. + * + * The standard local moving algorithm iterates over the nodes in a + * network. A node is moved to the cluster that results in the largest + * increase in the quality function. If the current cluster assignment of + * the node is already optimal, the node is not moved. The algorithm + * continues iterating over the nodes in a network until no more nodes can + * be moved. + * + * @param network Network + * @param clustering Clustering + * + * @return Boolean indicating whether the clustering has been improved + */ + public improveClustering (network: Network, clustering: Clustering): boolean { + if (network.nNodes === 1) { + return false + } + + let update = false + + const clusterWeights = new Array(network.nNodes).fill(0) + const nNodesPerCluster = new Array(network.nNodes).fill(0) + for (let i = 0; i < network.nNodes; i++) { + clusterWeights[clustering.clusters[i]] += network.nodeWeights[i] + nNodesPerCluster[clustering.clusters[i]]++ + } + + let nUnusedClusters = 0 + const unusedClusters = new Array(network.nNodes - 1).fill(0) + for (let i = network.nNodes - 1; i >= 0; i--) { + if (nNodesPerCluster[i] === 0) { + unusedClusters[nUnusedClusters] = i + nUnusedClusters++ + } + } + + const nodeOrder = generateRandomPermutation(network.nNodes, this.random) + + /* + * Iterate over the nodeOrder array in a cyclical manner. When the end + * of the array has been reached, start again from the beginning. + * Continue iterating until none of the last nNodes node visits has + * resulted in a node movement. + */ + const edgeWeightPerCluster = new Array(network.nNodes).fill(0) + const neighboringClusters = new Array(network.nNodes).fill(0) + let nUnstableNodes = network.nNodes + let i = 0 + do { + const j = nodeOrder[i] + + const currentCluster = clustering.clusters[j] + + // Remove the currently selected node from its current cluster. + clusterWeights[currentCluster] -= network.nodeWeights[j] + nNodesPerCluster[currentCluster]-- + if (nNodesPerCluster[currentCluster] === 0) { + unusedClusters[nUnusedClusters] = currentCluster + nUnusedClusters++ + } + + /* + * Identify the neighboring clusters of the currently selected + * node, that is, the clusters with which the currently selected + * node is connected. An empty cluster is also included in the set + * of neighboring clusters. In this way, it is always possible that + * the currently selected node will be moved to an empty cluster. + */ + neighboringClusters[0] = unusedClusters[nUnusedClusters - 1] + let nNeighboringClusters = 1 + for (let k = network.firstNeighborIndices[j]; k < network.firstNeighborIndices[j + 1]; k++) { + const l = clustering.clusters[network.neighbors[k]] + if (edgeWeightPerCluster[l] === 0) { + neighboringClusters[nNeighboringClusters] = l + nNeighboringClusters++ + } + edgeWeightPerCluster[l] += network.edgeWeights[k] + } + + /* + * For each neighboring cluster of the currently selected node, + * calculate the increment of the quality function obtained by + * moving the currently selected node to the neighboring cluster. + * Determine the neighboring cluster for which the increment of the + * quality function is largest. The currently selected node will be + * moved to this optimal cluster. In order to guarantee convergence + * of the algorithm, if the old cluster of the currently selected + * node is optimal but there are also other optimal clusters, the + * currently selected node will be moved back to its old cluster. + */ + let bestCluster = currentCluster + let maxQualityValueIncrement = edgeWeightPerCluster[currentCluster] - network.nodeWeights[j] * clusterWeights[currentCluster] * this.resolution + for (let k = 0; k < nNeighboringClusters; k++) { + const l = neighboringClusters[k] + + const qualityValueIncrement = edgeWeightPerCluster[l] - network.nodeWeights[j] * clusterWeights[l] * this.resolution + if (qualityValueIncrement > maxQualityValueIncrement) { + bestCluster = l + maxQualityValueIncrement = qualityValueIncrement + } + + edgeWeightPerCluster[l] = 0 + } + + /* + * Move the currently selected node to its new cluster. Update the + * clustering statistics. + */ + clusterWeights[bestCluster] += network.nodeWeights[j] + nNodesPerCluster[bestCluster]++ + if (bestCluster === unusedClusters[nUnusedClusters - 1]) { + nUnusedClusters-- + } + nUnstableNodes-- + + /* + * If the new cluster of the currently selected node is different + * from the old cluster, some further updating of the clustering + * statistics is performed. + */ + if (bestCluster !== currentCluster) { + clustering.clusters[j] = bestCluster + if (bestCluster >= clustering.nClusters) { + clustering.nClusters = bestCluster + 1 + } + + nUnstableNodes = network.nNodes + + update = true + } + + i = (i < network.nNodes - 1) ? (i + 1) : 0 + } while (nUnstableNodes > 0) + + if (update) { + clustering.removeEmptyClusters() + } + + return update + } +} diff --git a/src/utils/arrays.ts b/src/utils/arrays.ts new file mode 100644 index 0000000..f04afae --- /dev/null +++ b/src/utils/arrays.ts @@ -0,0 +1,172 @@ +import Random from 'java-random' + +/** + * Calculates the sum of the values in an array. + * + * @param values Values + * @param beginIndex Begin index + * @param endIndex End index + * + * @returns Sum of values + */ +export function calcSum (values: number[]): number { + let sum = 0 + for (let i = 0; i < values.length; i++) { + sum += values[i] + } + return sum +} + +/** + * Calculates the sum of the values in an array, considering only array + * elements within a specified range. + * + * The sum is calculated over the elements `values[beginIndex], ..., + * values[endIndex - 1]`. + * + * @param values Values + * @param beginIndex Begin index + * @param endIndex End index + * + * @return Sum of values + */ +export function calcSumWithinRange (values: number[], beginIndex: number, endIndex: number): number { + let sum = 0 + for (let i = beginIndex; i < endIndex; i++) { + sum += values[i] + } + return sum +} + +/** + * Calculates the average of the values in an array. + * + * @param values Values + * + * @return Average value + */ +export function calcAverage (values: number[]): number { + return calcSum(values) / values.length +} + +/** + * Calculates the median of the values in an array. + * + * @param values Values + * + * @return Median value + */ +export function calcMedian (values: number[]): number { + const sortedValues = values.slice() + sortedValues.sort((a, b) => a - b) + let median: number + if (sortedValues.length % 2 === 1) { + median = sortedValues[(sortedValues.length - 1) / 2] + } else { + median = (sortedValues[sortedValues.length / 2 - 1] + sortedValues[sortedValues.length / 2]) / 2 + } + return median +} + +/** + * Calculates the minimum of the values in an array. + * + * @param values Values + * + * @return Minimum value + */ +export function calcMinimum (values: number[]): number { + let minimum = values[0] + for (let i = 1; i < values.length; i++) { + minimum = Math.min(minimum, values[i]) + } + return minimum +} + +/** + * Calculates the maximum of the values in an array. + * + * @param values Values + * + * @return Maximum value + */ +export function calcMaximum (values: number[]): number { + let maximum = values[0] + for (let i = 1; i < values.length; i++) { + maximum = Math.max(maximum, values[i]) + } + return maximum +} + +/** + * Creates a double array of random numbers. + * + * @param nElements Number of elements + * @param random Random number generator + * + * @return Array of random numbers + */ +export function createDoubleArrayOfRandomNumbers (nElements: number, random: Random = new Random()): number[] { + const values = new Array(nElements) + for (let i = 0; i < nElements; i++) { + values[i] = random.nextDouble() + } + return values +} + +/** + * Generates a random permutation. + * + * A random permutation is generated of the integers`0, ..., nElements - 1`. + * + * @param nElements Number of elements + * @param random Random number generator + * + * @return Random permutation + */ +export function generateRandomPermutation (nElements: number, random: Random = new Random()): number[] { + const permutation = new Array(nElements) + for (let i = 0; i < nElements; i++) { + permutation[i] = i + } + for (let i = 0; i < nElements; i++) { + const j = random.nextInt(nElements) + const k = permutation[i] + permutation[i] = permutation[j] + permutation[j] = k + } + return permutation +} + +/** + * Searches a range of the specified array of numbers for the specified value + * using the binary search algorithm. The array must be sorted prior to making + * this call. If it is not sorted, the results are undefined. If the range + * contains multiple elements with the specified value, there is no guarantee + * which one will be found. + * + * @param sortedValues Array to be searched (must be sorted) + * @param fromIndex Index of the first element (inclusive) to be searched + * @param toIndex Index of the last element (exclusive) to be searched + * @param key Value to be searched for + * + * @returns Index at which the key was found, or -n-1 if it was not found, + * where n is the index of the first value higher than key or length if + * there is no such value. + */ +export function binarySearch (sortedValues: number[], fromIndex: number, toIndex: number, key: number): number { + let low = fromIndex + let high = toIndex - 1 + while (low <= high) { + const mid = (low + high) >>> 1 + const midVal = sortedValues[mid] + if (midVal < key) { + low = mid + 1 + } else if (midVal > key) { + high = mid - 1 + } else { + return mid + } + } + return -(low + 1) +} diff --git a/src/utils/declaration.d.ts b/src/utils/declaration.d.ts new file mode 100644 index 0000000..f070578 --- /dev/null +++ b/src/utils/declaration.d.ts @@ -0,0 +1,33 @@ +declare module 'java-random' { + /** + * An almost complete implementation in JS of the `java.util.Random` class + * from J2SE, designed to so far as possible produce the same output + * sequences as the Java original when supplied with the same seed. + * See {@link https://www.npmjs.com/package/java-random} for more + * information. + */ + export default class Random { + /** + * Constructs a new random number generator. + * + * @param seedval Initial seed + */ + public constructor(seed?: number) + + /** + * Returns a pseudorandom double value between 0.0 and 1.0. + * + * @return Pseudorandom double value between 0.0 and 1.0 + */ + public nextDouble (): number + + /** + * Returns a pseudorandom int value (between zero and the specified upper bound). + * + * @param bound Upper bound (exclusive) + * + * @return Pseudorandom int value (between zero and the specified upper bound) + */ + public nextInt (bound?: number): number + } +} diff --git a/src/utils/fastMath.ts b/src/utils/fastMath.ts new file mode 100644 index 0000000..efc5211 --- /dev/null +++ b/src/utils/fastMath.ts @@ -0,0 +1,47 @@ +/** + * Calculates `exp(exponent)` using a fast implementation. + * + * @param exponent Exponent + * + * @return exp(exponent) + */ +export function fastExp (exponent: number): number { + if (exponent < -256) return 0 + + exponent = 1 + exponent / 256 + exponent *= exponent + exponent *= exponent + exponent *= exponent + exponent *= exponent + exponent *= exponent + exponent *= exponent + exponent *= exponent + exponent *= exponent + return exponent +} + +/** + * Calculates `base ^ exponent` using a fast implementation. + * + * @param base Base + * @param exponent Exponent + * + * @return base ^ exponent + */ +export function fastPow (base: number, exponent: number): number { + let power: number + if (exponent > 0) { + power = base + for (let i = 1; i < exponent; i++) { + power *= base + } + } else if (exponent < 0) { + power = 1 / base + for (let i = -1; i > exponent; i--) { + power /= base + } + } else { + power = 1 + } + return power +} diff --git a/src/utils/index.ts b/src/utils/index.ts new file mode 100644 index 0000000..fd23151 --- /dev/null +++ b/src/utils/index.ts @@ -0,0 +1,27 @@ +/** + * Provides miscellaneous utility functions. + * + * @module + */ +import Random from 'java-random' +import { + calcSum, calcSumWithinRange, calcAverage, calcMedian, calcMinimum, + calcMaximum, createDoubleArrayOfRandomNumbers, generateRandomPermutation, + binarySearch, +} from './arrays' +import { fastExp, fastPow } from './fastMath' + +export { + Random, + calcSum, + calcSumWithinRange, + calcAverage, + calcMedian, + calcMinimum, + calcMaximum, + createDoubleArrayOfRandomNumbers, + generateRandomPermutation, + binarySearch, + fastExp, + fastPow, +} diff --git a/svelte.config.js b/svelte.config.js new file mode 100644 index 0000000..bb7914b --- /dev/null +++ b/svelte.config.js @@ -0,0 +1,5 @@ +import autoPreprocess from 'svelte-preprocess' + +module.exports = { + preprocess: autoPreprocess(), +} diff --git a/tsconfig.app.json b/tsconfig.app.json new file mode 100644 index 0000000..e3862fc --- /dev/null +++ b/tsconfig.app.json @@ -0,0 +1,13 @@ +{ + "extends": "./tsconfig.base.json", + "compilerOptions": { + "paths": { + "networkanalysis-ts": ["src"], + "networkanalysis-ts/*": ["src/*"], + }, + "rootDir": "./", + "baseUrl": "./", + "outDir": "./dist", + "declaration": false, + }, +} diff --git a/tsconfig.base.json b/tsconfig.base.json new file mode 100644 index 0000000..4ab68d3 --- /dev/null +++ b/tsconfig.base.json @@ -0,0 +1,38 @@ +{ + "compileOnSave": false, + "compilerOptions": { + "declaration": false, + + "downlevelIteration": true, + "alwaysStrict": true, + "noImplicitAny": true, + "strictNullChecks": true, + "strictPropertyInitialization": true, + "strictFunctionTypes": true, + "noImplicitThis": true, + "strictBindCallApply": true, + + "allowSyntheticDefaultImports": true, + "sourceMap": true, + "module": "esnext", + "moduleResolution": "node", + "resolveJsonModule": true, + "emitDecoratorMetadata": true, + "noEmit": true, + "experimentalDecorators": true, + "target": "es5", + "typeRoots": [ + "node_modules/@types" + ], + "lib": [ + "es2017", + "es2016", + "es2015", + "dom" + ] + }, + "exclude": [ + "node_modules", + "*/node_modules/*" + ] +} \ No newline at end of file diff --git a/tsconfig.lib.json b/tsconfig.lib.json new file mode 100644 index 0000000..2612d58 --- /dev/null +++ b/tsconfig.lib.json @@ -0,0 +1,13 @@ +{ + "extends": "./tsconfig.base.json", + "compilerOptions": { + "rootDir": "./src", + "baseUrl": "./src", + "outDir": "./lib", + "declaration": true, + }, + "exclude": [ + "app", + "index.ts" + ] +} \ No newline at end of file diff --git a/typedoc.json b/typedoc.json new file mode 100644 index 0000000..676d88a --- /dev/null +++ b/typedoc.json @@ -0,0 +1,13 @@ +{ + "$schema": "https://typedoc.org/schema.json", + "tsconfig": "tsconfig.lib.json", + "entryPoints": ["./src/index.ts", "./src/run/index.ts", "./src/utils/index.ts"], + "out": "docs", + "sort":[ + "static-first", + "instance-first", + "visibility", + "source-order" + ], + "readme": "./src/README.md" +}