Abstract
We propose a graph-layout based method for detecting communities in networks. We first project the graph onto a Euclidean space using Fruchterman-Reingold algorithm, a force-based graph drawing algorithm. We then cluster the vertices according to Euclidean distance. The idea is a form of dimension reduction. The graph drawing in two or more dimensions provides a heuristic decision as whether vertices are connected by a short path approximated by their Euclidean distance. We study community detection for both disjoint and overlapping communities. For the case of disjoint communities, we use k-means clustering. For the case of overlapping communities, we use fuzzy-c means algorithm. We evaluate the performance of our different algorithms for varying parameters and number of iterations. We compare the results to several state of the art community detection algorithms, each of which clusters the graph directly or indirectly according to geodesic distance. We show that, for non-trivially small graphs, our method is both effective and efficient. We measure effectiveness using modularity when the communities are not known in advance and precision when the communities are known in advance. We measure efficiency with running time. The running time of our algorithms can be controlled by the number of iterations of the Fruchterman-Reingold algorithm.
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References
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical society, Series B (1977)
Aslam, J.A., Pelekhov, E., Rus, D.: The star clustering algorithm for static and dynamic information organization. J. Graph Algorithms Appl. 8, 95–129 (2004)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
Chen, W., Liu, Z., Sun, X., Wang, Y.: A game-theoretic framework to identify overlapping communities in social networks. Data Min. Knowl. Discov. (2010)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Du, N., Wu, B., Pei, X., Wang, B., Xu, L.: Community detection in large-scale social networks. In: WebKDD/SNA-KDD, ACM (2007)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U.S.A. (1998)
Etling, B., Kelly, J., Faris, R., John, P.: Mapping the arabic blogosphere: Politics, culture, and dissent. Berkman Center Research Publication (2006-06) (2009)
Fortunato, S., Castellano, C.: Community structure in graphs. In: Encyclopedia of Complexity and Systems Science, pp. 1141–1163 (2009)
Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Exper. 21(11), 1129–1164 (1991)
Gergely Palla, I.F., Derenyi, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature (2005)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences (2002)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Physical Review E 78 (2008)
Lancichinetti, A., Radicchi, F., Ramasco, J.J., Fortunato, S.: Finding statistically significant communities in networks. CoRR (2010)
Lloyd, S.P.: Least squares quantization in pcm. IEEE Transactions on Information Theory 28, 129–137 (1982)
Macropol, K., Can, T., Singh, A.K.: Rrw: repeated random walks on genome-scale protein networks for local cluster discovery. BMC Bioinformatics (2009)
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 284–293. Springer, Heidelberg (2005)
Reddy, P.K., Kitsuregawa, M., Sreekanth, P., Rao, S.S.: A graph based approach to extract a neighborhood customer community for collaborative filtering. In: Bhalla, S. (ed.) DNIS 2002. LNCS, vol. 2544, pp. 188–200. Springer, Heidelberg (2002)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences of the United States of America 105 (2008)
Schaeffer, S.E.: Graph clustering. Computer Science Review 1(1), 27–64 (2007)
van Dongen, S.: Graph Clustering by Flow Simulation. PhD thesis (2000)
Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 25–36. Springer, Heidelberg (2012)
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Song, Y., Bressan, S. (2013). Force-Directed Layout Community Detection. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40285-2_36
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DOI: https://doi.org/10.1007/978-3-642-40285-2_36
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