Network Structure Analysis > Source Codes

• Detecting Overlapping Communities

  This is a code implementation release for DOCA, short for Detecting Overlapping Communities Algorithm, from the paper "Overlapping Community Structures and Their Detection on Social Networks" by Nam P. Nguyen, Thang N. Dinh, Dung T. Nguyen and My T. Thai published in The 3rd IEEE Int. Conf. on Social Computing (SOCIALCOM) 2011.
  
  The program is written in Visual C++ Express, version 2010. This code is implemented for undirected unweighted graphs. Five real social traces that were used in the above paper are provided. Please check the readme file for details.
  
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• Adaptively Finding Overlapping Community Structure

  This is a code implementation release for AFOCS, short for Adaptively Finding Overlapping Community Structure, from the paper "Overlapping Communities in Dynamic Networks: Their Detection and Mobile Applications" by Nam P. Nguyen, Thang N. Dinh, Sindhura Tokala and My T. Thai published in The 17th Int. Conf. on Mobile Computing and Networking (MOBICOM) 2011.
  
  The program is written in Visual C++ Express, version 2010. This code is implemented for undirected unweighted graphs. One real social trace along with synthesized data that were used in the above paper are provided. Please check the readme file for details.
  
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• Community Vulnerability Assessment

  This is a code implementation release for CVA, short for Community Vulnerability Assessment, from the paper "Are Communities As Strong As We Think?" by Md Abdul Alim, Alan Kuhnle, and My T. Thai published in the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2014.
  
  The program finds out critical edges in an undirected graph to break k important communities. The code is written in Java. Three real social trace along with synthesized data that were used in the above paper are provided. Please check the readme file for detail.
  
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• Maximizing Modularity

  This is a code implementation release for solving the linear programming for modularity maximization, from the paper T. N. Dinh and M. T. Thai, Community Detection in Scale-free Networks: Approximation Algorithms for Maximizing Modularity , IEEE Journal on Selected Areas in Communications: Special Issue on Network Science (JSAC), vol. 31, no. 6, pp. 997--1006, June 2013.
  
  Please check the readme file for more details.
  
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