Abstract
Dynamic complex networks are used to model the evolving relationships between entities in widely varying fields of research such as epidemiology, ecology, sociology, and economics. In the study of complex networks, a network is said to have community structure if it divides naturally into groups of vertices with dense connections within groups and sparser connections between groups. Detecting the evolution of communities within dynamically changing networks is crucial to understanding complex systems. In this paper, we develop a fast community detection algorithm for real-time dynamic network data. Our method takes advantage of community information from previous time steps and thereby improves efficiency while maintaining the quality of community detection. Our experiments on citation-based networks show that the execution time improves as much as 30% (average 13%) over static methods.
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References
Voevodski, K., Teng, S.H., Xia, Y.: Finding local communities in protein networks. BMC Bioinformatics 10(10), 297 (2009)
Vazquez, A., Dobrin, R., Sergi, D., Eckmann, J.P., Oltvai, Z.N., Barabási, A.L.: The topological relationship between the large-scale attributes and local interaction patterns of complex networks. PNAS 101, 17940–17945 (2004)
Watts, D., Strogatz, S.: Collective dynamics of small world networks. Nature 393(6684) (441), 42–440 (1998)
Albert, R., Jeong, H., Barabasi, A.L.: Diameter of the world-wide web. Nature 401, 130–131 (1999)
Newman, M., Park, J.: Why social networks are different from other types of networks. Phys. Rev. E 68(036122), 36122 (2003)
Newman, M.: Assortative mixing in networks. Phys. Rev. Lett. 89, 208701 (2002)
Boguna, M., Pastor-Satorras, R., Vespignani: Epidemic spreading in complex networks with degree correlations. In: Statistical Mechanics of Complex Networks. Lecture Notes in Physics, vol. 625, pp. 127–147 (2003)
Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)
Porter, M., Mucha, P.J., Newman, M.E.J., Friend, A.J.: Community structure in the united states house of representatives. Physica A 386, 414–438 (2007)
Barabasi, A.L., Jeong, H., Ravasz, E., Neda, Z., Schuberts, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A 311, 590–614 (2002)
Atkins, K., Chen, J., Anil Kumar, V.S., Marathe, A.: Structure of electrical networks: A graph theory based analysis. International Journal of Critical Infrastructures 5, 265–284 (2009)
Girvan, M., Newman, M.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)
Newman, M.: Detecting community structure in networks. Eur. Phys. J. B 38, 321–330 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Fortunato, S., Barthlemy, M.: Resolution limit in community detection. PNAS 104(1), 36–41 (2007)
Good, B.H., de Montjoye, Y., Clauset, A.: The performance of modularity maximization in practical contexts. Phys. 82, 046106 (2010)
Steinhaeuser, K., Chawla, N.V.: Identifying and evaluating community structure in complex networks. Pattern Recognition Letters 31(5), 413–421 (2010)
Gaertler, M.: Clustering. Network Anal., 178–215 (2005)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 66111 (2004)
Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1275–1276. ACM, New York (2007)
Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 717–726 (2007)
Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.: Incremental spectral clustering with application to monitoring of evolving blog communities. In: SIAM Int. Conf. on Data Mining, pp. 261–272 (2007)
Leung, I.X.Y., Hui, P., Liò, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E 79, 066107 (2009)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.-P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–878 (2010)
Bader, D.A., Amos-Binks, A., Chavarrsa-Miranda, D., Hastings, C., Madduri, K., Poulos, S.C.: STINGER: Spatio-Temporal Interaction Networks and Graphs (STING) Extensible Representation, Tech. rep., Georgia Institute of Technology (2009)
Saad, Y.: Iterative Methods for Sparse Linear Systems. PWS Publishing Company (1995)
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Bansal, S., Bhowmick, S., Paymal, P. (2011). Fast Community Detection for Dynamic Complex Networks. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds) Complex Networks. Communications in Computer and Information Science, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25501-4_20
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DOI: https://doi.org/10.1007/978-3-642-25501-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25500-7
Online ISBN: 978-3-642-25501-4
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