Self-organizing map of complex networks for community detection

Detecting communities from complex networks is an important issue and has attracted attention of researchers in many fields. It is relevant to social tasks, biological inquiries, and technological problems since various networks exist in these systems. This paper proposes a new self-organizing map (SOM) based approach to community detection. By adopting a new operation and a new weight-updating scheme, a complex network can be organized into dense subgraphs according to the topological connection of each node by the SOM algorithm. Extensive numerical experiments show that the performance of the SOM algorithm is good. It can identify communities more accurately than existing methods. This method can be used to detect communities not only in undirected networks, but also in directed networks and bipartite networks.

[1]  Stefan Bornholdt,et al.  Detecting fuzzy community structures in complex networks with a Potts model. , 2004, Physical review letters.

[2]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[3]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[5]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[6]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

[7]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Shi-Hua Zhang,et al.  Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures , 2008, Neurocomputing.

[9]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[10]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[11]  Luonan Chen,et al.  Quantitative function for community detection. , 2008 .

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

[13]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[14]  Sarika Jalan,et al.  Random matrix analysis of complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Roger Guimerà,et al.  Module identification in bipartite and directed networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Mika Gustafsson,et al.  Comparison and validation of community structures in complex networks , 2006 .

[17]  Xiang-Sun Zhang,et al.  Modularity optimization in community detection of complex networks , 2009 .

[18]  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.

[19]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Shihua Zhang,et al.  Uncovering fuzzy community structure in complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Amedeo Caflisch,et al.  Multistep greedy algorithm identifies community structure in real-world and computer-generated networks , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[23]  Luonan Chen,et al.  Biomolecular Networks: Methods and Applications in Systems Biology , 2009 .

[24]  Rui-Sheng Wang,et al.  Optimization analysis of modularity measures for network community detection , 2008 .

[25]  Xiang-Sun Zhang,et al.  Neural networks in optimization , 2000 .

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