HICODE

Edited by Yingru Li.

Compiling

Before compiling, the following software should be installed in your system.

• g++
• Xeltex
• R packages

Compile both the base algorithms and the HICODE program.

$ sh compile.sh

After compiling, you should see a binary file

$ ./Main

How to Run

Rename the graph file to "graph", and put it into directory

$ ./data/GRAPH_NAME/

• The graph file's first line indicates the number of vertices.
• The following lines contain 'i j w' representing an edge between (i,j) with weight w.
• Then write a config file for the graph , named "GRAPH_NAME.config" . See configuration details in the next section.

Run HICODE using command:

$ ./Main GRAPH_NAME.config

Example

Run 2-layers synthetic data:

$ ./Main config/synl2.config

You will find the community detection results in:

$ ./result_directory/{maxLayer, maxOriginal}*.gen

You can find the ground truth communities in:

$ ./data/synl2/Layer1.gen
$ ./data/synl2/Layer2.gen

Configuration Details

In config file, each row include a pair of Parameter:Value . see 'config/synl2.config' as an example.

Parameter	Description
DATA_DIR	The data directory
RESULT_DIR	The result directory
Ground_Truth	The ground truth (only the partition type should be listed)
SingleLayer_Method	The base algorithm of the framework. 'Modularity'/'Infomap'
Frameworks	The type of framework , separated by ';' if want multiple. 'Remove'/'Reduce++'
Framework_CommunitySizeThres	The threshold for leftover
Number_Of_Layers	The choosen Layer Number
Number_Of_Iteration	The choosen Iteration Number
WeightedGraph	TRUE/FALSE indicating if the weighted graph should be used

TIPS

The weakening method 'ReduceWeight' in paper indicates the 'Frameworks' should be 'Reduce++' and the 'WeightedGraph' should be TRUE.

Code Structure

Framework

The current framework is implemented in some header files like 'Framework_Reduce.h' and the base class is 'Framework.h'.

The most important function that needs to be implemented is 'calcNextLayerGraph(Graph cur,Community comm)', which gives the current graph and a set of communitie, return value is the reduced graph.

Base Algorithms

The current framework is implemented in some header files like 'SingleLayer_Modularity.h' and the base class is 'SingleLayer_Method.h'.

The most important function that needs to be implemented is 'generateCommunity(string graphFile,string communityFile)', which takes in the graph file and uses the algorithm to generate the communities and saves to a communityFile.

Announcements

Licence

Based on the article "Hidden Community Detection in Social Networks" Copyright (C) 2017 He. Kun, Li. Yingru, Soundarajan. Sucheta, Hopcroft. John E. [arXiv]

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Notification

Please email to us or setup an issue if you have any problems or find any bugs.

Please cite our papers if you use the code in your paper:

        @article{heli2018hidden,
        title = "Hidden community detection in social networks",
        journal = "Information Sciences",
        volume = "425",
        number = "Supplement C",
        pages = "92 - 106",
        year = "2018",
        issn = "0020-0255",
        doi = "https://doi.org/10.1016/j.ins.2017.10.019",
        url = "http://www.sciencedirect.com/science/article/pii/S0020025517310101",
        author = "Kun He and Yingru Li and Sucheta Soundarajan and John E. Hopcroft",
        keywords = "Community detection, Hidden community, Structure mining, Social networks"
        }

        @ARTICLE{2017arXiv170207462H,
          author = {{He}, K. and {Li}, Y. and {Soundarajan}, S. and {Hopcroft}, J.~E.},
          title = "{Hidden Community Detection in Social Networks}",
          journal = {ArXiv e-prints},
          archivePrefix = "arXiv",
          eprint = {1702.07462},
          keywords = {Computer Science - Social and Information Networks, Physics - 
          Physics and Society, Statistics - Machine Learning},
          year = 2017,
          month = feb,
          adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170207462H},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
        }

       @article{DBLP:journals/corr/HeSCHH15,
          author    = {Kun He and
          Sucheta Soundarajan and
          Xuezhi Cao and
          John E. Hopcroft and
          Menglong Huang},
          title     = {Revealing Multiple Layers of Hidden Community Structure in Networks},
          journal   = {CoRR},
          volume    = {abs/1501.05700},
          year      = {2015},
          url       = {http://arxiv.org/abs/1501.05700},
          timestamp = {Mon, 02 Feb 2015 14:12:25 +0100},
          biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/HeSCHH15},
          bibsource = {dblp computer science bibliography, http://dblp.org}
        }

Acknowledgement

In the program, we incorporates some open source codes as baseline algorithms from the following websites:

• Link Communities

• OSLOM

• Louvain Method for Modularity

• Infomap