Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman’s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

[1]  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.

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

[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.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[5]  R. May,et al.  Infection dynamics on scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  R W Hockney,et al.  Computer Simulation Using Particles , 1966 .

[7]  John Scott.  Social Network Analysis , 1988 .

[8]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[9]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[10]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[11]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Bernardo A. Huberman,et al.  Email as spectroscopy: automated discovery of community structure within organizations , 2003 .

[13]  Neo D. Martinez,et al.  Simple rules yield complex food webs , 2000, Nature.

[14]  Donald Greenspan.  Arithmetic applied mathematics , 1980 .

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

[16]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

[17]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[18]  Fang Wu,et al.  Finding communities in linear time: a physics approach , 2003, ArXiv.

[19]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

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

[21]  L. Greengard,et al.  A Fast Adaptive Multipole Algorithm for Particle Simulations , 1988 .

[22]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[23]  C. Lee Giles,et al.  Self-Organization and Identification of Web Communities , 2002, Computer.

[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]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[26]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .