An overlapping network community partition algorithm based on semi-supervised matrix factorization and random walk

Abstract The discovery of community structure is the basis of understanding the topology structure and social function of the network. It is also an important factor for recommendation technology, information dissemination, event prediction, and more. In this paper, we consider the structure and characteristics of the social network and propose an algorithm based on semi-supervised matrix factorization and random walk. The proposed method first calculates the transition probability between nodes through the topology of the network. The random walk model is then used to obtain the final walk probability, and the feature matrix is constructed. At the same time, we combine a priori content information in the network to build a must-link matrix and a cannot-link matrix. We then merge them into the feature matrix of the random walk to form a new feature matrix. Finally, the expectation of the number of edges is defined according to the factorized membership matrix. Results demonstrate the effectiveness and better performance of our method.

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

[2]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[3]  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.

[4]  T. Nepusz,et al.  Fuzzy communities and the concept of bridgeness in complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Jure Leskovec,et al.  Empirical comparison of algorithms for network community detection , 2010, WWW '10.

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

[7]  Hong Cheng,et al.  Graph Clustering Based on Structural/Attribute Similarities , 2009, Proc. VLDB Endow..

[8]  Padhraic Smyth,et al.  A Spectral Clustering Approach To Finding Communities in Graph , 2005, SDM.

[9]  Bin Wu,et al.  A link clustering based overlapping community detection algorithm , 2013, Data Knowl. Eng..

[10]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[11]  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.

[12]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[13]  Steve Gregory,et al.  Fuzzy overlapping communities in networks , 2010, ArXiv.

[14]  Fei Wang,et al.  Community discovery using nonnegative matrix factorization , 2011, Data Mining and Knowledge Discovery.

[15]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

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

[17]  Sergey Brin,et al.  Reprint of: The anatomy of a large-scale hypertextual web search engine , 2012, Comput. Networks.

[18]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[19]  Dayou Liu,et al.  Hierarchical community detection with applications to real-world network analysis , 2013, Data Knowl. Eng..

[20]  A. Arenas,et al.  Community detection in complex networks using extremal optimization. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  François Fouss,et al.  Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.

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

[23]  Zhong-Yuan Zhang,et al.  Enhanced Community Structure Detection in Complex Networks with Partial Background Information , 2012, Scientific Reports.

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

[25]  Yixin Cao,et al.  Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization , 2013, Scientific Reports.

[26]  Xiaoke Ma,et al.  Semi-supervised clustering algorithm for community structure detection in complex networks , 2010 .

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

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

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

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

[31]  Wenbo Zhao,et al.  PageRank and Random Walks on Graphs , 2010 .

[32]  Huaikou Miao,et al.  Overlap Community Detection Based on Node Convergence Degree , 2016, 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).