Abstract
Community detection or graph clustering is an important problem in the analysis of computer networks, social networks, biological networks and many other natural and artificial networks. These networks are in general very large and, thus, finding hidden structures and functional modules is a very hard task. In this paper we propose new data structures and a new implementation of a well known agglomerative greedy algorithm to find community structure in large networks, the CNM algorithm. The experimental results show that the improved data structures speedup the method by a large factor, for large networks, making it competitive with other state of the art algorithms.
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References
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Brandes, U., et al.: On finding graph clusterings with maximum modularity. In: Brandstädt, A., Kratsch, D., Müller, H. (eds.) WG 2007. LNCS, vol. 4769, pp. 121–132. Springer, Heidelberg (2007)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69, 066133 (2004)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E 70, 066111 (2004)
Chung, F., Lu, L., Dewey, T.G., Galas, D.J.: Duplication models for biological networks. Journal of Computational Biology 10(5), 677–687 (2003)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004)
Guimerà , R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks. Physical Review E 70(2), 025101 (2004)
Agarwal, G., Kempe, D.: Modularity-maximizing communities via mathematical programming. The European Physical Journal B 66(3), 409–418 (2008)
Bhan, A., Galas, D.J., Dewey, T.G.: A duplication growth model of gene expression networks. Bioinformatics 18(11), 1486–1493 (2002)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
Lusseau, D., et al.: The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology 54(4), 396–405 (2003)
Duch, J., Arenas, A.: Community identification using extremal optimization. Physical Review E 72, 027104 (2005)
Jeong, H., Mason, S., Barabási, A.L., Oltvai, Z.N.: Centrality and lethality of protein networks. Nature 411(6833), 41–42 (2001)
Schuetz, P., Caflisch, A.: Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Physical Review E 77(4), 46112 (2008)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics P10008 (2008)
Noack, A., Rotta, R.: Multi-level algorithms for modularity clustering. In: Experimental Algorithms. LNCS, vol. 5526, pp. 257–268. Springer, Heidelberg (2009)
Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks. In: International World Wide Web Conference, pp. 1275–1276. ACM, New York (2007)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Physical Review E 74, 016110 (2006)
Danon, L., Duch, J., Diaz-Guilera, A., Arenas, A.: The effect of size heterogeneity on community identification in complex networks. Journal of Statistical Mechanics P11010 (2006)
Fortunato, S., Barthélemy, M.: Resolution limit in community detection. PNAS 104(1), 36–41 (2007)
Kumpula, J.M., Saramäki, J., Kaski, K., Kertész, J.: Limited resolution in complex network community detection with Potts model approach. The European Physical Journal B - Condensed Matter and Complex Systems 56(1), 41–45 (2007)
Feng, Z., Xu, X., Yuruk, N., Schweiger, T.A.J.: A novel similarity-based modularity function for graph partitioning. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 385–396. Springer, Heidelberg (2007)
Rosvall, M., Bergstrom, C.T.: An information-theoretic framework for resolving community structure in complex networks. PNAS 104(18), 7327 (2007)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 111–118 (2008)
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Francisco, A.P., Oliveira, A.L. (2011). On Community Detection in Very Large 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_21
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DOI: https://doi.org/10.1007/978-3-642-25501-4_21
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