KA8_Neo4jGraphPageRank.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Page Rank\n",
    "PageRank is widely recognized as a way of detecting influential nodes in a graph.\n",
    "It is different to other centrality algorithms because the influence of a node depends on the influence of its neighbours.\n",
    "\n",
    "The underlying mathematics of PageRank is based on random walks on networks.\n",
    "We can think of a random walk as the way that a person might manually traverse a graph.\n",
    "We start from any node and then either follow one of its relationships to another node or jump to another node in the graph and continue our exploration.\n",
    "The PageRank of a node is the probability that it is visited during this random walk.\n",
    "More influential nodes will be visited more often.\n",
    "\n",
    "First we'll import the Neo4j driver and Pandas libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neo4j.v1 import GraphDatabase, basic_auth\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let's create an instance of the Neo4j driver which we'll use to execute our queries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "host = os.environ.get(\"NEO4J_HOST\", \"bolt://localhost\") \n",
    "user = os.environ.get(\"NEO4J_USER\", \"neo4j\")\n",
    "password = os.environ.get(\"NEO4J_PASSWORD\", \"neo\")\n",
    "driver = GraphDatabase.driver(host, auth=basic_auth(user, password))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a sample graph that we'll run the algorithm against.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stats: {}\n"
     ]
    }
   ],
   "source": [
    "create_graph_query = '''\n",
    "MERGE (home:Page {name:'Home'})\n",
    "MERGE (about:Page {name:'About'})\n",
    "MERGE (product:Page {name:'Product'})\n",
    "MERGE (links:Page {name:'Links'})\n",
    "MERGE (a:Page {name:'Site A'})\n",
    "MERGE (b:Page {name:'Site B'})\n",
    "MERGE (c:Page {name:'Site C'})\n",
    "MERGE (d:Page {name:'Site D'})\n",
    "\n",
    "MERGE (home)-[:LINKS]->(about)\n",
    "MERGE (about)-[:LINKS]->(home)\n",
    "MERGE (product)-[:LINKS]->(home)\n",
    "MERGE (home)-[:LINKS]->(product)\n",
    "MERGE (links)-[:LINKS]->(home)\n",
    "MERGE (home)-[:LINKS]->(links)\n",
    "MERGE (links)-[:LINKS]->(a)\n",
    "MERGE (a)-[:LINKS]->(home)\n",
    "MERGE (links)-[:LINKS]->(b)\n",
    "MERGE (b)-[:LINKS]->(home)\n",
    "MERGE (links)-[:LINKS]->(c)\n",
    "MERGE (c)-[:LINKS]->(home)\n",
    "MERGE (links)-[:LINKS]->(d)\n",
    "MERGE (d)-[:LINKS]->(home)\n",
    "'''\n",
    "\n",
    "with driver.session() as session:\n",
    "    result = session.write_transaction(lambda tx: tx.run(create_graph_query))\n",
    "    print(\"Stats: \" + str(result.consume().metadata.get(\"stats\", {})))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can run the algorithm by executing the following query:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>page</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>3.233877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>About</td>\n",
       "      <td>1.060435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Product</td>\n",
       "      <td>1.060435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Links</td>\n",
       "      <td>1.060435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Site A</td>\n",
       "      <td>0.329035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Site B</td>\n",
       "      <td>0.329035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Site C</td>\n",
       "      <td>0.329035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Site D</td>\n",
       "      <td>0.329035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      page     score\n",
       "0     Home  3.233877\n",
       "1    About  1.060435\n",
       "2  Product  1.060435\n",
       "3    Links  1.060435\n",
       "4   Site A  0.329035\n",
       "5   Site B  0.329035\n",
       "6   Site C  0.329035\n",
       "7   Site D  0.329035"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streaming_query = \"\"\"\n",
    "CALL algo.pageRank.stream('Page', 'LINKS', {iterations:20, dampingFactor:0.85})\n",
    "YIELD node, score\n",
    "RETURN node.name AS page,score\n",
    "ORDER BY score DESC\n",
    "\"\"\"\n",
    "\n",
    "with driver.session() as session:\n",
    "    result = session.read_transaction(lambda tx: tx.run(streaming_query))        \n",
    "    df = pd.DataFrame([r.values() for r in result], columns=result.keys())\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we might expect, the Home page has the highest PageRank because it has incoming links from all other pages.\n",
    "We can also see that it's not only the number of incoming links that is important, but also the importance of the pages behind those links."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also call a version of the algorithm that will store the result as a property on a\n",
    "node. This is useful if we want to run future queries that use the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "write_query = \"\"\"\n",
    "CALL algo.pageRank('Page', 'LINKS',\n",
    "  {iterations:20, dampingFactor:0.85, write: true,writeProperty:\"pagerank\"})\n",
    "YIELD nodes, iterations, loadMillis, computeMillis, writeMillis, dampingFactor, write, writeProperty\n",
    "\"\"\"\n",
    "\n",
    "with driver.session() as session:\n",
    "    session.write_transaction(lambda tx: tx.run(write_query))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Visualisation\n",
    "\n",
    "Sometimes a picture can tell more than a table of results and this is often the case with graph algorithms. \n",
    "Let's see how to create a graph visualization using neovis.js.\n",
    "\n",
    "First we'll create a div into which we will generate the visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">                \n",
       ".output_wrapper, .output {\n",
       "    height:auto !important;\n",
       "    max-height:600px;\n",
       "}\n",
       ".output_scroll {\n",
       "    box-shadow:none !important;\n",
       "    webkit-box-shadow:none !important;\n",
       "}\n",
       "\n",
       "#viz {\n",
       "    width: 300px;\n",
       "    height: 350px;\n",
       "    font: 22pt arial;\n",
       "}\n",
       "</style>  \n",
       "<div id=\"viz\"></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style type=\"text/css\">                \n",
    ".output_wrapper, .output {\n",
    "    height:auto !important;\n",
    "    max-height:600px;\n",
    "}\n",
    ".output_scroll {\n",
    "    box-shadow:none !important;\n",
    "    webkit-box-shadow:none !important;\n",
    "}\n",
    "\n",
    "#viz {\n",
    "    width: 300px;\n",
    "    height: 350px;\n",
    "    font: 22pt arial;\n",
    "}\n",
    "</style>  \n",
    "<div id=\"viz\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to define the query that the visualization will be generated from, along with config \n",
    "that describes which properties will be used for node size, node colour, and relationship width. \n",
    "\n",
    "We'll then define a JavaScript variable that contains all our parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.jsonGraph={\"query\": \"MATCH (p1:Page)-[r:LINKS]->(p2:Page) RETURN *\", \"labels\": {\"Page\": {\"caption\": \"name\", \"size\": \"pagerank\"}}, \"relationships\": {\"LINKS\": {\"thickness\": \"weight\", \"caption\": false}}, \"host\": \"bolt://localhost\", \"user\": \"neo4j\", \"password\": \"neo\"};"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import Javascript\n",
    "import json\n",
    "from scripts.algo import viz_config, render_image\n",
    "\n",
    "config = viz_config(\"Page Rank\")\n",
    "query = config[\"query\"]\n",
    "labels_json = config[\"labels_json\"]\n",
    "relationships_json = config[\"relationships_json\"]\n",
    "\n",
    "json_graph = {\n",
    "    \"query\": query,\n",
    "    \"labels\": labels_json,\n",
    "    \"relationships\": relationships_json,\n",
    "    \"host\": host,\n",
    "    \"user\": user,\n",
    "    \"password\": password\n",
    "}\n",
    "\n",
    "Javascript(\"\"\"window.jsonGraph={};\"\"\".format(json.dumps(json_graph)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to call neovis.js and generate our graph visualisation. \n",
    "The following code will create an interactive graph into the div defined above.\n",
    "It will also extract an image representation of the graph and display that in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var output_area = this;\n",
       "requirejs(['neovis.js'], function(NeoVis){    \n",
       "    var config = {\n",
       "      container_id: \"viz\",\n",
       "      server_url: window.jsonGraph.host,\n",
       "      server_user: window.jsonGraph.user,\n",
       "      server_password: window.jsonGraph.password,\n",
       "      labels: window.jsonGraph.labels,\n",
       "      relationships: window.jsonGraph.relationships,\n",
       "      initial_cypher: window.jsonGraph.query\n",
       "    };\n",
       "        \n",
       "    let viz = new NeoVis.default(config);\n",
       "    viz.render();\n",
       "    \n",
       "    viz.onVisualizationRendered(function(ctx) {\n",
       "      let imageSrc = ctx.canvas.toDataURL();\n",
       "      let kernel = IPython.notebook.kernel;\n",
       "      let command = \"image_src = '\" + imageSrc + \"'\";\n",
       "      kernel.execute(command);\n",
       "      \n",
       "      var cell_element = output_area.element.parents('.cell');\n",
       "      var cell_idx = Jupyter.notebook.get_cell_elements().index(cell_element);\n",
       "      var cell = Jupyter.notebook.get_cell(cell_idx+1);\n",
       "      cell.set_text(\"render_image(image_src)\")\n",
       "      cell.execute();\n",
       "    });\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "var output_area = this;\n",
    "requirejs(['neovis.js'], function(NeoVis){    \n",
    "    var config = {\n",
    "      container_id: \"viz\",\n",
    "      server_url: window.jsonGraph.host,\n",
    "      server_user: window.jsonGraph.user,\n",
    "      server_password: window.jsonGraph.password,\n",
    "      labels: window.jsonGraph.labels,\n",
    "      relationships: window.jsonGraph.relationships,\n",
    "      initial_cypher: window.jsonGraph.query\n",
    "    };\n",
    "        \n",
    "    let viz = new NeoVis.default(config);\n",
    "    viz.render();\n",
    "    \n",
    "    viz.onVisualizationRendered(function(ctx) {\n",
    "      let imageSrc = ctx.canvas.toDataURL();\n",
    "      let kernel = IPython.notebook.kernel;\n",
    "      let command = \"image_src = '\" + imageSrc + \"'\";\n",
    "      kernel.execute(command);\n",
    "      \n",
    "      var cell_element = output_area.element.parents('.cell');\n",
    "      var cell_idx = Jupyter.notebook.get_cell_elements().index(cell_element);\n",
    "      var cell = Jupyter.notebook.get_cell(cell_idx+1);\n",
    "      cell.set_text(\"render_image(image_src)\")\n",
    "      cell.execute();\n",
    "    });\n",
    "});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img id=\"viz-image\" width=\"300px\" 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\" />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "render_image(image_src)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}