Weighted Graph Clustering for Community Detection of Large Social Networks

Abstract This study mainly focuses on the methodology of weighted graph clustering with the purpose of community detection for large scale networks such as the users’ relationship on Internet social networks. Most of the networks in the real world are weighted networks, so we proposed a graph clustering algorithm based on the concept of density and attractiveness for weighted networks, including node weight and edge weight. With deep analysis on the Sina micro-blog user network and Renren social network, we defined the user's core degree as node weight and users’ attractiveness as edge weight, experiments of community detection were done with the algorithm, the results verify the effectiveness and reliability of the algorithm. The algorithm is designed to make some breakthrough on the time complexity of Internet community detection algorithm, because the research is for large social networks. And the another advantage is that the method does not require to specify the number of clusters.

[1]  Weixiong Zhang,et al.  An Efficient Spectral Algorithm for Network Community Discovery and Its Applications to Biological and Social Networks , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[2]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[3]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Malik Magdon-Ismail,et al.  SSDE-Cluster: Fast Overlapping Clustering of Networks Using Sampled Spectral Distance Embedding and GMMs , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[6]  Yousef Saad,et al.  Dense Subgraph Extraction with Application to Community Detection , 2012, IEEE Transactions on Knowledge and Data Engineering.

[7]  Huiru Zheng,et al.  Evaluation repeated random walks in community detection of social networks , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[8]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[10]  Jianhua Chen,et al.  Detecting Communities Using Social Ties , 2010, 2010 IEEE International Conference on Granular Computing.

[11]  Dong Wang,et al.  Analysis and Comparison of Interaction Patterns in Online Social Network and Social Media , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[12]  Hans-Peter Kriegel,et al.  Density-based community detection in social networks , 2011, 2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application.

[13]  Ioannis Stavrakakis,et al.  ISCoDe: A framework for interest similarity-based community detection in social networks , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  Padhraic Smyth,et al.  A Spectral Clustering Approach To Finding Communities in Graph , 2005, SDM.

[15]  Richi Nayak,et al.  Finding and Matching Communities in Social Networks Using Data Mining , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[16]  Nitesh V. Chawla,et al.  Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.