FOX

Community detection is a hot topic for researchers in the fields of graph theory, social networks, and biological networks. Generally speaking, a community refers to a group of densely linked nodes in the network. Nodes usually have more than one community label, indicating their multiple roles or functions in the network. Unfortunately, existing solutions aiming at overlapping community detection are not capable of scaling to large-scale networks with millions of nodes and edges. In this article, we propose a fast-overlapping-community-detection algorithm—FOX. In the experiment on a network with 3.9 millions nodes and 20 millions edges, the detection finishes in 41 min and provides the most qualified results. The second-fastest algorithm, however, takes almost five times longer to run. As for another network with 22 millions nodes and 127 millions edges, our algorithm is the only one that can provide an overlapping community detection result and it only takes 533 min. Our algorithm is a typical heuristic algorithm, measuring the closeness of a node to a community by counting the number of triangles formed by the node and two other nodes in the community. We also extend the exploitation of triangle to open-triangle, which enlarges the scale of the detected communities.

[1]  Kevin Chen-Chuan Chang,et al.  Learning Community Embedding with Community Detection and Node Embedding on Graphs , 2017, CIKM.

[2]  S. Athey,et al.  A Theory of Community Formation and Social Hierarchy , 2016 .

[3]  Junming Shao,et al.  Community Detection based on Distance Dynamics , 2015, KDD.

[4]  Meng Wang,et al.  Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework , 2015, Proc. VLDB Endow..

[5]  David Dominguez-Sal,et al.  Distributed Community Detection with the WCC Metric , 2014, WWW.

[6]  Michel Crampes,et al.  Overlapping Community Detection Optimization and Nash Equilibrium , 2014, WIMS.

[7]  Jeffrey Xu Yu,et al.  Influential Community Search in Large Networks , 2015, Proc. VLDB Endow..

[8]  Erwan Le Martelot,et al.  Fast multi-scale detection of overlapping communities using local criteria , 2014, Computing.

[9]  Jeffrey Xu Yu,et al.  Querying k-truss community in large and dynamic graphs , 2014, SIGMOD Conference.

[10]  Josep-Lluís Larriba-Pey,et al.  High quality, scalable and parallel community detection for large real graphs , 2014, WWW.

[11]  Ulrik Brandes,et al.  Triangle Listing Algorithms: Back from the Diversion , 2014, ALENEX.

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

[13]  David M Blei,et al.  Efficient discovery of overlapping communities in massive networks , 2013, Proceedings of the National Academy of Sciences.

[14]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[15]  Lars Backstrom,et al.  Balanced label propagation for partitioning massive graphs , 2013, WSDM.

[16]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[17]  Ling Huang,et al.  Evolution of social-attribute networks: measurements, modeling, and implications using google+ , 2012, Internet Measurement Conference.

[18]  Josep-Lluís Larriba-Pey,et al.  Shaping communities out of triangles , 2012, CIKM.

[19]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[20]  Santo Fortunato,et al.  Finding Statistically Significant Communities in Networks , 2010, PloS one.

[21]  Wei Chen,et al.  A game-theoretic framework to identify overlapping communities in social networks , 2010, Data Mining and Knowledge Discovery.

[22]  Tom L. Roberts,et al.  Proposing the online community self-disclosure model: the case of working professionals in France and the U.K. who use online communities , 2010, Eur. J. Inf. Syst..

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

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

[25]  S. Kiesler,et al.  Applying Common Identity and Bond Theory to Design of Online Communities , 2007 .

[26]  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.

[27]  Illés J. Farkas,et al.  CFinder: locating cliques and overlapping modules in biological networks , 2006, Bioinform..

[28]  L. Miller,et al.  Self-disclosure and liking: a meta-analytic review. , 1994, Psychological bulletin.

[29]  Deborah A. Prentice,et al.  Asymmetries in Attachments to Groups and to their Members: Distinguishing between Common-Identity and Common-Bond Groups , 1994 .