Merge pull request #1 from Xiejiadong/main

Data and Codes of "Influence Minimization via Blocking Strategies"

Author: ge660

AUTHORS

@@ -0,0 +1,6 @@
+Jiadong Xie             <jdxie@se.cuhk.edu.hk>
+Fan Zhang               <zhangf@gzhu.edu.cn>
+Kai Wang                <w.kai@sjtu.edu.cn>
+Jialu Liu               <jialuliu@sjtu.edu.cn>
+Xuemin Lin              <xuemin.lin@sjtu.edu.cn>
+Wenjie Zhang            <wenjie.zhang@unsw.edu.au>

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Jiadong XIE
+
+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.

README.md

@@ -1 +1,96 @@
-# 2024.0591
+[![INFORMS Journal on Computing Logo](https://INFORMSJoC.github.io/logos/INFORMS_Journal_on_Computing_Header.jpg)](https://pubsonline.informs.org/journal/ijoc)
+
+<!-- # 2024.0591 -->
+
+# Influence Minimization via Blocking Strategies
+
+This archive is distributed in association with the [INFORMS Journal on Computing](https://pubsonline.informs.org/journal/ijoc) under the [MIT License](LICENSE).
+
+The software and data in this repository are a snapshot of the software and data that were used in the research reported in the paper [Influence minimization via blocking strategies](https://doi.org/10.1287/ijoc.2024.0591) by Jiadong Xie, Fan Zhang, Kai Wang, Jialu Liu, Xuemin Lin, and Wenjie Zhang.
+
+## Cite
+
+To cite the contents of this repository, please cite both the paper and this repo, using their respective DOIs.
+
+[https://doi.org/10.1287/ijoc.2024.0591](https://doi.org/10.1287/ijoc.2024.0591)
+
+[https://doi.org/10.1287/ijoc.2024.0591.cd](https://doi.org/10.1287/ijoc.2024.0591.cd)
+
+Below is the BibTex for citing this snapshot of the repository.
+
+```
+@article{IM2024,
+  author       = {Jiadong Xie and
+                  Fan Zhang and
+                  Kai Wang and
+                  Jialu Liu and
+                  Xuemin Lin and
+                  Wenjie Zhang},
+  publisher =     {INFORMS Journal on Computing},
+  title =         {Influence Minimization via Blocking Strategies},
+  year =          {2024},
+  doi =           {10.1287/ijoc.2024.0591.cd},
+  url =           {https://github.com/INFORMSJoC/2024.0591},
+}  
+```
+
+
+
+## Datasets
+
+The experiments are performed on 8 datasets sourced from [SNAP](http://snap.stanford.edu), also accessible through the files under [/data/](/data/).
+
+In dataset files, the first line specifies the vertex count `n` and edge count `m`. Each subsequent line features two integers `u v` representing a directed edge $(u,v)$.
+
+Prior to executing our algorithms, it is essential to first process the downloaded datasets from [SNAP](http://snap.stanford.edu) using the provided data processing scripts at [/scripts/DataProcess.cpp](/scripts/DataProcess.cpp).
+Specifically, utilize `g++ -o DataProcess DataProcess.cpp -std=c++11 -O3` for compilation, run the code with `./DataProcess`, and input the dataset name when run.
+
+
+## Run Algorithms
+
+### Compile
+
+Compile the codes either by utilizing the scripts under [/scripts/compile.sh](./scripts/compile.sh) or by directly employing the subsequent commands.
+
+```shell
+g++ -o AdvancedGreedy AdvancedGreedy.cpp -std=c++11 -O3
+g++ -o GreedyReplace GreedyReplace.cpp -std=c++11 -O3
+```
+
+### Algorithms
+
+* **AdvancedGreedy (AG)**: Algorithm 3 that contains Algorithm 2 to accelerate the baseline greedy algorithm.
+
+* **GreedyReplace (GR)**: Our GreedyReplace algorithm (Algorithm 4).
+
+All the algorithms can be run as follows.
+
+For example, we use `./GreedyReplace` to run the code, and then input the name of the dataset, the influence model, the propagation model, the number of sources and the budget by the keyboard.
+
+```shell
+dataset: sample_graph.txt
+influence model (0: IC model; 1: LT model): 0
+propagation model (0: TR model; 1: WC model): 0
+number of sources : 3
+budget: 3
+```
+Note that we use the same rand parameter to ensure all the algorithms have the same seed set: `mt19937 rand_num(20220708)`.
+
+### Results
+
+The program will print the sources which are chosen randomly and the time for loading the dataset.
+
+```shell
+source: 24 28 36 
+Finish loading dataset. time : 0.000569s.
+```
+
+The experiment results will be saved in [/results](/results/), e.g., [/results/GR-IC-TR-sample_graph.txt](/results/GR-IC-TR-sample_graph.txt).
+
+Three numbers in the results mean `budget`, `expected spread` and `running time (s)`, respectively.
+
+Detailed results for all the tested instances can be found in [/results/results.pdf](/results/results.pdf).
+
+#### Environment
+
+The experiments in our paper are performed on a CentOS Linux serve (Release 7.5.1804) with Quad-Core Intel Xeon CPU (E5-2640 v4 @ 2.20GHz) and 128G memory. All the algorithms are implemented in C++. The source code is compiled by GCC(7.3.0) under O3 optimization.