Label propagation algorithm based on local cycles for community detection

Label propagation algorithm (LPA) has been proven to be an extremely fast method for community detection in large complex networks. But an important issue of the algorithm has not yet been properly addressed that random update orders in label propagation process hamper the algorithm robustness of algorithm. We note that when there are multiple maximal labels among a node neighbors' labels, choosing a node' label from which there is a local cycle to the node instead of a random node' label can avoid the labels propagating among communities at random. In this paper, an improved LPA based on local cycles is given. We have evaluated the proposed algorithm on computer-generated networks with planted partition and some real-world networks whose community structure are already known. The result shows that the performance of the proposed approach is even significantly improved.

[1]  N. Alves.  Unveiling community structures in weighted networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  A. Arenas,et al.  Models of social networks based on social distance attachment. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Marko Bajec,et al.  Robust network community detection using balanced propagation , 2011, ArXiv.

[4]  Dong Liu,et al.  Fuzzy overlapping community detection based on local random walk and multidimensional scaling , 2013 .

[5]  M. Barber,et al.  Detecting network communities by propagating labels under constraints. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  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.

[7]  Marko Bajec,et al.  Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

[9]  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.

[10]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

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

[12]  Yiannis Kompatsiaris,et al.  Community detection in Social Media , 2012, Data Mining and Knowledge Discovery.

[13]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

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

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

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

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

[18]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[19]  Steve Gregory,et al.  Finding overlapping communities in networks by label propagation , 2009, ArXiv.

[20]  T. Murata,et al.  Advanced modularity-specialized label propagation algorithm for detecting communities in networks , 2009, 0910.1154.

[21]  T. Vicsek,et al.  Directed network modules , 2007, physics/0703248.

[22]  M. Newman,et al.  Identifying the role that animals play in their social networks , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.