An Efficient Approach of Overlapping Communities Search

A great deal of research has been dedicated to discover overlapping communities, as in most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. However, most work has focused on community detection, which takes the whole graph as input and derives all communities at one time. Community detection can only be used in offline analysis of networks and it is quite costly, not flexible and can not support dynamically evolving networks. Online community search which only finds overlapping communities containing given nodes is a flexible and light-weight solution, and also supports dynamic graphs very well. Thus, in this paper, we study an efficient solution for overlapping community search problem. We propose an exact algorithm whose performance is highly improved by considering boundary node limitation and avoiding duplicate computations of multiple input nodes, and we also propose three approximate strategies which trade off the efficiency and quality, and can be adopted in different requirements. Comprehensive experiments are conducted and demonstrate the efficiency and quality of the proposed algorithms.

[1]  R. Lambiotte,et al.  Line graphs, link partitions, and overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  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.

[3]  James P. Bagrow.  Evaluating local community methods in networks , 2007, 0706.3880.

[4]  A. Clauset.  Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Aristides Gionis,et al.  The community-search problem and how to plan a successful cocktail party , 2010, KDD.

[6]  Yiannis Kompatsiaris,et al.  Bridge Bounding: A Local Approach for Efficient Community Discovery in Complex Networks , 2009, 0902.0871.

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

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

[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]  Maurice Tchuente,et al.  Local Community Identification in Social Networks , 2012, Parallel Process. Lett..

[11]  Kyomin Jung,et al.  LinkSCAN*: Overlapping community detection using the link-space transformation , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[12]  A. Folkesson.  IT and society , 2013 .

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

[14]  Feng Luo,et al.  Exploring Local Community Structures in Large Networks , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[15]  Haixun Wang,et al.  Online search of overlapping communities , 2013, SIGMOD '13.

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

[17]  Mauro Brunato,et al.  On Effectively Finding Maximal Quasi-cliques in Graphs , 2008, LION.