Improving Node Similarity for Discovering Community Structure in Complex Networks

Community detection is to detect groups consisting of densely connected nodes, and having sparse connections between them. Many researchers indicate that detecting community structures in complex networks can extract plenty of useful information, such as the structural features, network properties, and dynamic characteristics of the community. Several community detection methods introduced different similarity measures between nodes, and their performance can be improved. In this paper, we propose a community detection method based on an improvement of node similarities. Our method initializes a level for each node and assigns nodes into a community based on similarity between nodes. Then it selects core communities and expands those communities by layers. Finally, we merge communities and choose the best community in the network. The experimental results show that our method achieves state-of-the-art performance.

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

[2]  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.

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

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

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

[6]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[7]  David Lusseau,et al.  The emergent properties of a dolphin social network , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[8]  Jure Leskovec,et al.  Community Detection in Networks with Node Attributes , 2013, 2013 IEEE 13th International Conference on Data Mining.

[9]  Hui Chen,et al.  A Method for Community Detection of Complex Networks Based on Hierarchical Clustering , 2015, Int. J. Distributed Sens. Networks.

[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]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.

[13]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

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

[17]  M. Fiedler.  Algebraic connectivity of graphs , 1973 .

[18]  Yunming Ye,et al.  MultiComm: Finding Community Structure in Multi-Dimensional Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[19]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[20]  Jian-Guo Liu,et al.  Detecting community structure in complex networks via node similarity , 2010 .

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

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

[23]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[25]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

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

[27]  Randy Goebel,et al.  Detecting Communities in Social Networks Using Max-Min Modularity , 2009, SDM.

[28]  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.

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

[30]  Santo Fortunato,et al.  Community detection in networks: Structural communities versus ground truth , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  My T. Thai,et al.  Community Detection in Scale-Free Networks: Approximation Algorithms for Maximizing Modularity , 2013, IEEE Journal on Selected Areas in Communications.

[32]  Jari Saramäki,et al.  Characterizing the Community Structure of Complex Networks , 2010, PloS one.

[33]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.

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

[35]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

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