Community detection in large-scale social networks

ABSTRACT

Recent years have seen that WWW is becoming a flourishing social media which enables individuals to easily share opinions, experiences and expertise at the push of a single button. With the pervasive usage of instant messaging systems and the fundamental shift in the ease of publishing content, social network researchers and graph theory researchers are now concerned with inferring community structures by analyzing the linkage patterns among individuals and web pages. Although the investigation of community structures has motivated many diverse algorithms, most of them are unsuitable for large-scale social networks because of the computational cost. Moreover, in addition to identify the possible community structures, how to define and explain the discovered communities is also significant in many practical scenarios.

In this paper, we present the algorithm ComTector(Community DeTector) which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world. This algorithm does not require any priori knowledge about the number or the original division of the communities. Because real networks are often large sparse graphs, its running time is thus O(C × Tri²), where C is the number of the detected communities and Tri is the number of the triangles in the given network for the worst case. Then we propose a general naming method by combining the topological information with the entity attributes to define the discovered communities. With respected to practical applications, ComTector is challenged with several real life networks including the Zachary Karate Club, American College Football, Scientific Collaboration, and Telecommunications Call networks. Experimental results show that this algorithm can extract meaningful communities that are agreed with both of the objective facts and our intuitions.

References

1. J. Abello, M. Resende, and S. Sudarsky. Massive quasi-clique detection. In 5th Latin American Symposium on Theoretical Informatics, pages 598--612, 2002.
2. C. Bron and J. Kerbosch. Finding all cliques of an undirected graph. Communications of the ACM, 16:575--577, 1973.
3. O. V. Burton. Computing in the Social Sciences and Humanities. University of Illinois Press, 2006.
4. A. Clauset, M. Newman, and C. Moore. Finding community structure in very large networks. Physical Review E, 70(066111), 2004.
5. A. Clauset, M. Newman, and C. Moore. Finding local community structure in networks. Physical Review E, 72(026132), 2004.
6. L. Donetti and M. Miguel. Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics, pages 100--102, 2004.
7. N. Du, B. Wu, and B. Wang. A parallel algorithm for enumerating all maximal cliques in complex networks. In The 6th ICDM2006 Mining Complex Data Workshop, pages 320--324. IEEE CS, December 2006.
8. J. Duch and A. Arenas. Community detection in complex networks using extremal optimization. Physical Review E, 72(027104), August 2005.
9. M. Girvan and M. Newman. Community structure in social and biological networks. PNAS, 99(12):7821--7826, June 2002.
10. M. Girvan and M. Newman. Finding and evaluating community structure in networks. Physical Review E, 69(026113), 2004.
11. I. Gunes and H. Bingol. Community detection in complex networks using agents. In AAMAS2007, 2007.
12. J. Han and M. Kamber. Data Mining: Concepts and Techniques, 2nd ed. Morgan Kaufmann Publishers, 2006.
13. R. Milo and S. Itzkovitz. Network motifs: Simple building blocks of complex networks. Science, 298:824--827, 2002.
14. M. Newman. Detecting community structure in networks. The European Physical Journal B-Condensed Matter, 38:321--330, 2004.
15. M. Newman. Modularity and community structure in networks. PNAS, 103(23):8577--8582, June 2006.
16. G. Palla, I. Dernyi, and I. Farkas. Uncovering the overlapping community structure of complex network in nature and society. Nature, 435:814--818, June 2005.
17. J. Pei, D. Jiang, and A. Zhang. On mining cross-graph quasi-cliques. In The 12th ACM SIGKDD, pages 228--237, 2006.
18. P. Pons and M. Latapy. Computing communities in large networks using random walks. In ISCIS2005, pages 284--293, 2005.
19. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi. Defining and identifying communities in networks. PNAS, 101(9):2658--2663, March 2004.
20. M. Rosvall and C. T. Bergstrom. An information-theoretic framework for resolving community structure in complex networks. PNAS, 104(18):7327--7331, May 2007.
21. J. Scott. Social Network Analysis: A Handbook. Sage Publications, London, 2002.
22. C. Song, M. Havlin, and H. Makse. A self-similarity of complex networks. Nature, 433(7024):392--395, 2005.
23. A. Vlczquez, R. Dobrin, S. Sergi, J. Eckmann, Z. Oltvai, and A. Barablcsi. The topological relationship between the large-scale attributes and local interaction patterns of complex networks. PNAS, 101(52):17940--17945, 2004.
24. S. Wasserman and K. Faust. Social Network Analysis. Cambridge University Press, Cambridge, 1994.
25. B. Wu and X. Pei. A parallel algorithm for enumerating all the maximal k-plexes. In PAKDD07 Workshops, May 2007.
26. Z. Zeng, J. Wang, and G. Karypis. Coherent closed quasi-clique discovery from large dense graph databases. In The 12th ACM SIGKDD, pages 797--802, 2006.
27. http://www-personal.umich.edu/~mejn/
28. http://vlado.fmf.unilj.si/pub/networks/pajek/data/gphs.htm
29. V. Batagelj and A. Mrvar. Some Analyses of Erdos Collaboration Graph. http://vlado.fmf.unilj.si/pub/networks/doc/erdos/erdos.pdf

Index Terms

• Information systems

  • Information systems applications

    • Data mining