• 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.

    Download the [Source Code]
  • 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.

    Download the [Source Code]
  • 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.

    Download the [Source Code]
  • 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.

    Download the [Source Code]
  • Viral Advertising in OSN

    The VirAds algorithm overcomes severe scalability problem in the natural greedy algorithm by favoring the vertex that can activate the most number of edges as well as considering the number of active neighbors around each vertex at the same time.

    Specifically, at early steps, the algorithm behaves similarly to the degree-based heuristics that favors vertices with high degree. However, when a certain number of vertices is selected,VirAds will make the selection based on the information within d-hop neighbors around the considered vertices rather than only one-hop neighbors as in the degree-based heuristic. Then in each iteration, we select the node with high-effectiveness, which is defined by the total number of activated edges and activated nodes. After this, it will update the status of remaining vertices by using CELF, which further help it speed up.

    Refer: T. N. Dinh, H. Zhang, D. T. Nguyen, and M. T. Thai, Cost-effective Viral Marketing for Time-critical Campaigns in Large-scale Social Networks, IEEE/ACM Transactions on Networking (ToN), DOI: 10.1109/TNET.2013.2290714, 2013

    Download the [Source Code]
  • Coupling Networks

    Algorithms for normalization of networks with overlapping users, generation of coupling networks, and generation of synthesis networks are included here. Please refer the readme file and comments in the source codes for more details.

    Refer: D. T. Nguyen, H. Zhang, S. Das, M. T. Thai, and T. N. Dinh, Least Cost Influence in Multiplex Social Networks: Model Representation and Analysis, in Proceedings of the IEEE Int Conference on Data Mining (ICDM), 2013.

    Download the [Source Code]