Abstract
Local community detection aims at finding a community structure starting from a seed (i.e., a given vertex) in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC for local community detection. It estimates the similarity between vertices via the investigation on vertices’ neighborhoods, and reveals a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our GMAC algorithm.
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References
Bagrow, J.P.: Evaluating local community methods in networks. Journal of Statistical Mechanics-Theory and Experiment 5, P05001 (2008)
Bagrow, J.P., Bollt, E.M.: Local method for detecting communities. Physical Review E 72(4), 046108 (2005)
Chen, H.H., Gou, L., Zhang, X.L., Giles, C.L.: Discovering missing links in networks using vertex similarity measures. In: SAC, pp. 138–143 (2012)
Chen, J., Zaïane, O.R., Goebel, R.: Local community identification in social networks. In: ASNAM, pp. 237–242 (2009)
Clauset, A.: Finding local community structure in networks. Physical Review E 72(2), 26132 (2005)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E 70(6), 066111 (2004)
Coscia, M., Rossetti, G., Giannotti, F., Pedreschi, D.: Demon: a local-first discovery method for overlapping communities. In: KDD, pp. 615–623 (2012)
Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010)
Gargi, U., Lu, W., Mirrokni, V.S., Yoon, S.: Large-scale community detection on youtube for topic discovery and exploration. In: ICWSM (2011)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)
Gleich, D.F., Seshadhri, C.: Vertex neighborhoods, low conductance cuts, and good seeds for local community methods. In: KDD, pp. 597–605 (2012)
Huang, J.B., Sun, H.L., Liu, Y.G., Song, Q.B., Weninger, T.: Towards online multiresolution community detection in large-scale networks. PLOS One 6(8), e23829 (2011)
Lancichinetti, A., Fortunato, S.: Community detection algorithms: A comparative analysis. Physical Review E 80(5), 056117 (2009)
Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 033015 (2009)
Li, K., Pang, Y.: A vertex similarity probability model for finding network community structure. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part I. LNCS, vol. 7301, pp. 456–467. Springer, Heidelberg (2012)
Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. In: WI, pp. 233–239 (2006)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74(3), 036104 (2006)
Rees, B.S., Gallagher, K.B.: Overlapping community detection by collective friendship group inference. In: ASNAM, pp. 375–379 (2010)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state of the art and comparative study. ACM Computing Surveys 45(4) (2013)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
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Ma, L., Huang, H., He, Q., Chiew, K., Wu, J., Che, Y. (2013). GMAC: A Seed-Insensitive Approach to Local Community Detection. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_26
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DOI: https://doi.org/10.1007/978-3-642-40131-2_26
Publisher Name: Springer, Berlin, Heidelberg
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