Research and Evaluation on Modularity Modeling in Community Detecting of Complex Network Based on Information Entropy

Detecting the community of complex networks became the hot research fields of Graph Ming in recent years and most community detecting methods current try to find correct community structure basing on optimization of Modularity Q.In this article, the author constructs a new theoretic model of Q based on information entropy by simulation and evaluation on some classic dataset and comparison with the classic social network[12] experimental results such as karate network, musicians network, email network and dolphin network by GN and Fast GN algorithm to cast some new light on community detecting. In the implementation, the author developed a visualization evalution tool to analyze the community relationship in entities of complex networks in large scale mobile calling networks and gained some novel results in this area with visualization evaluation tool.

[1]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[3]  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.

[4]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

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

[6]  Massimo Marchiori,et al.  Method to find community structures based on information centrality. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Shen Hua,et al.  Information Bottleneck Based Community Detection in Network: Information Bottleneck Based Community Detection in Network , 2009 .

[8]  Martin Rosvall,et al.  An information-theoretic framework for resolving community structure in complex networks , 2007, Proceedings of the National Academy of Sciences.

[9]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[10]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[11]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Zhou Aoying,et al.  Linkage Analysis for the World Wide Web and Its Application: A Survey , 2003 .

[13]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[14]  Sun Xue.  The Application of Entropy in Complex Network Connectivity Research , 2005 .