Detecting communities in social networks by local affinity propagation with grey relational analysis

Purpose – The purpose of this paper is to discover social communities from the social networks by propagating affinity messages among members in a localized way. The affinity between any two members is computed by grey relational analysis method. Design/methodology/approach – First, the responsibility messages and the availability messages are restricted to be broadcasted only among a node and its neighbours, i.e. the nodes that connected to it directly. In this way, both the time complexity and the space complexity can be reduced to be near linear to the network size. The near-linear time and space complexity is quite important for social network analysis because social networks are generally very large. Second, instead of the widely used Euclidean distance, the grey relational degree is adopted in the calculation of node similarity, because the latter is more suitable for the discovery of the hidden relations among the nodes. On the basis of the two improvements, a new social community detection algorit...

[1]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[2]  Yong Liu,et al.  Dynamic information aggregation decision-making methods based on variable precision rough set and grey clustering , 2014, Grey Syst. Theory Appl..

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

[4]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[5]  Ken Wakita,et al.  Finding community structure in mega-scale social networks: [extended abstract] , 2007, WWW '07.

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

[7]  Malik Magdon-Ismail,et al.  Defining and Discovering Communities in Social Networks , 2012 .

[8]  Andrea Lancichinetti,et al.  Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Erkan Kose,et al.  Grey relational analysis between energy consumption and economic growth , 2013, Grey Syst. Theory Appl..

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

[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]  Naixue Xiong,et al.  A social community detection algorithm based on parallel grey label propagation , 2016, Comput. Networks.

[13]  Panos M. Pardalos,et al.  Handbook of Optimization in Complex Networks , 2012 .

[14]  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.

[15]  Xie Zhengguang,et al.  Image denoising method based on grey relational threshold , 2013 .

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

[17]  Yu Jun,et al.  A New Definition of Modularity for Community Detection in Complex Networks , 2012 .

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