A novel dynamical community detection algorithm based on weighting scheme

Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

[1]  Yue Zhao,et al.  FAST ACCURATE COMMUNITY DETECTION BASED ON DYNAMICS PROCESS AND STABILITY OPTIMIZATION , 2012 .

[2]  Yong Wang,et al.  Community structure detection based on Potts model and network's spectral characterization , 2012 .

[3]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[5]  M. Hasler,et al.  Network community-detection enhancement by proper weighting. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  A. Barabasi,et al.  Comparable system-level organization of Archaea and Eukaryotes , 2001, Nature Genetics.

[7]  Benjamin H. Good,et al.  Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

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

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

[11]  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.

[12]  Yong Zhang,et al.  The detection of community structure in network via an improved spectral method , 2009 .

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

[14]  Haijun Zhou.  Distance, dissimilarity index, and network community structure. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[16]  Lin Wang,et al.  Spatial epidemiology of networked metapopulation: an overview , 2014, bioRxiv.

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

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

[19]  Xiang-Sun Zhang,et al.  Analysis of stability of community structure across multiple hierarchical levels , 2015, ArXiv.

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

[21]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[22]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.

[23]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.