HICODE

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 director

$ ./data/GRAPH_NAME/

The graph file's first line indicating the number of vertex.
The following lines containing '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 headfiles like 'Framework_Reduce.h' and the base class is 'Framework.h'.

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

Base Algorithms

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

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

Announcements

Licence

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:

JHL-HUST / HiCode