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Clustering Hypergraphs for Discovery of Overlapping Communities in Folksonomies

  • Abhijnan Chakraborty
  • Saptarshi Ghosh
Chapter
Part of the Modeling and Simulation in Science, Engineering and Technology book series (MSSET)

Abstract

Some of the most popular sites in the Web today are the social tagging systems or folksonomies (e.g. Delicious, Flickr LastFm) where the users share resources and collaboratively annotate those resources with meaningful tags. This helps in the search and the organization of the vast amount of resources. Folksonomies are modelled as tripartite user-resource-tag hypergraphs to study their network properties. Detecting communities of similar nodes from such networks is a challenging and well-studied problem. However, most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, nodes are often associated with multiple overlapping communities. Users have multiple topical interests, and the same resource is often tagged with semantically different tags. The few attempts to detect overlapping communities work on projections of the hypergraph, which results in significant loss of the information contained in the original tripartite structure. In this chapter, we present “Overlapping Hypergraph Clustering” algorithm which detects overlapping communities in folksonomies using the complete tripartite hypergraph structure. The algorithm converts a hypergraph into its corresponding weighted line graph, using measures of hyperedge similarity. Then simple nonoverlapping communities are detected from the line graph, which in turn produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the “Overlapping Hypergraph Clustering” algorithm can detect better community structures in folksonomies as compared to the existing state-of-the-art algorithms.

Keywords

Betweenness Centrality Line Graph Community Detection Normalize Mutual Information Multiple Community 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1].
    Y.-Y. Ahn, J.P. Bagrow, S. Lehmann, Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)CrossRefGoogle Scholar
  2. [2].
    J. Baumes, M.K. Goldberg, M.S. Krishnamoorthy, M.M. Ismail, N. Preston, Finding communities by clustering a graph into overlapping subgraphs, in Proc. IADIS Conference on Applied Computing, pp. 97–104, 2005Google Scholar
  3. [3].
    V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech. Theor Exp. 2008(10), (2008)Google Scholar
  4. [4].
    M. Brinkmeier, J. Werner, S. Recknagel, Communities in graphs and hypergraphs, in Proc. ACM Conference on Information and Knowledge Management (CIKM), 2007Google Scholar
  5. [5].
    S.R. Bulo, M. Pelillo, A game-theoretic approach to hypergraph clustering. Adv. Neural Inform. Process. Syst. 22, 1571–1579 (2009)Google Scholar
  6. [6].
    I. Cantador, P. Brusilovsky, T. Kuflik, 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011), in Proc. ACM Conference on Recommender Systems (RecSys), 2011Google Scholar
  7. [7].
    C. Cattuto, C. Schmitz, A. Baldassarri, V.D.P. Servedio, V. Loreto, A. Hotho, M. Grahl, G. Stumme, Network properties of folksonomies. AI Comm. 20(4), 245–262 (2007)MathSciNetGoogle Scholar
  8. [8].
    A. Chakraborty, Credibility measurement of users in e-learning forums, in Proc. National Convention of Computer Engineers, February 2012Google Scholar
  9. [9].
    A. Chakraborty, S. Ghosh, N. Ganguly, Detecting overlapping communities in folksonomies, in Proc. ACM Hypertext Conference, June 2012Google Scholar
  10. [10].
    A. Clauset, M.E.J. Newman, C. Moore, Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  11. [11].
    T.S. Evans, R. Lambiotte, Line graphs, link partitions, and overlapping communities. Phys. Rev. E 80, 016105 (2009)CrossRefGoogle Scholar
  12. [12].
    S. Fortunato, Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  13. [13].
    S. Ghosh, P. Kane, N. Ganguly, Identifying overlapping communities in folksonomies or tripartite hypergraphs, in Proc. ACM Conference on World Wide Web (WWW) companion volume, pp. 39–40, Mar 2011Google Scholar
  14. [14].
    M. Girvan, M.E.J. Newman, Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetMATHCrossRefGoogle Scholar
  15. [15].
    M. Girvan, M.E.J. Newman, Finding and evaluating community structure in networks. Phys. Rev. E 69 (2004)Google Scholar
  16. [16].
    S. Gregory, Finding overlapping communities using disjoint community detection algorithms, in Complex Networks, vol. 207 of Studies in Computational Intelligence (Springer, Berlin, 2009), pp. 47–61Google Scholar
  17. [17].
    S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes. Inform. Syst. 25(5), 345–366 (2000)CrossRefGoogle Scholar
  18. [18].
    R. Guimera, M. Sales-Pardo, L.A.N. Amaral, Module identification in bipartite and directed networks. Phys. Rev. E 76, 036102 (2007)CrossRefGoogle Scholar
  19. [19].
    T.G. Kolda, B.W. Bader, Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetMATHCrossRefGoogle Scholar
  20. [20].
    I. Konstas, V. Stathopoulos, J.M. Jose, On social networks and collaborative recommendation, in Proc. ACM SIGIR Conference, pp. 195–202, 2009Google Scholar
  21. [21].
    A. Lancichinetti, S. Fortunato, Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80(1), 9 (2009)Google Scholar
  22. [22].
    A. Lancichinetti, S. Fortunato, Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 056117 (2009)CrossRefGoogle Scholar
  23. [23].
    A. Lancichinetti, S. Fortunato, J. Kertesz, Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11, 033015 (2009)CrossRefGoogle Scholar
  24. [24].
    J. Leskovec, K.J. Lang, A. Dasgupta, M.W. Mahoney, Statistical properties of community structure in large social and information networks, in Proc. ACM Conference on World Wide Web (WWW), 2008Google Scholar
  25. [25].
    Y.-R. Lin, J. Sun, P. Castro, R. Konuru, H. Sundaram, A. Kelliher, Metafac: community discovery via relational hypergraph factorization, in Proc. ACM SIGKDD Conference, pp. 527–536, 2009Google Scholar
  26. [26].
    M. McPherson, L. Smith-Lovin, J. Cook, Birds of a feather: Homophily in social networks. Ann. Rev. Sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  27. [27].
    T. Murata, Detecting communities from social tagging networks based on tripartite modularity, in Proc. Workshop on Link Analysis in Heterogeneous Information Networks, July 2011Google Scholar
  28. [28].
    N. Neubauer, K. Obermayer, Towards community detection in k-partite k-uniform hypergraphs, in Proc. Workshop on Analyzing Networks and Learning with Graphs, pp. 1–9, 2009Google Scholar
  29. [29].
    M.E.J. Newman, Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)CrossRefGoogle Scholar
  30. [30].
    V. Nicosia, G. Mangioni, V. Carchiolo, M. Malgeri, Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech. Theor Exp. 3, 03024 (2008)Google Scholar
  31. [31].
    G. Palla, I. Derenyi, I. Farkas, T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  32. [32].
    S. Papadopoulos, Y. Kompatsiaris, A. Vakali, A graph-based clustering scheme for identifying related tags in folksonomies, in Proc. Conference on Data Warehousing and Knowledge Discovery (DaWaK), pp. 65–76, 2010Google Scholar
  33. [33].
    M. Rosvall, C.T. Bergstrom, Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105, 1118–1123 (2008)CrossRefGoogle Scholar
  34. [34].
    A. Vazquez, Finding hypergraph communities: a Bayesian approach and variational solution. J. Stat. Mech. Theor Exp. 2009, P07006 (2009)CrossRefGoogle Scholar
  35. [35].
    X. Wang, L. Tang, H. Gao, H. Liu, Discovering overlapping groups in social media, in Proc. IEEE Conference on Data Mining (ICDM), pp. 569–578, 2010Google Scholar
  36. [36].
    S. Xu, S. Bao, B. Fei, Z. Su, Y. Yu, Exploring folksonomy for personalized search, in Proc. ACM SIGIR Conference, pp. 155–162, 2008Google Scholar
  37. [37].
    D. Zhou, J. Huang, B. Scholkopf, Learning with hypergraphs: clustering, classification, and embedding, in Proc. Advances in Neural Information Processing Systems, 2006Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Microsoft Research IndiaBangaloreIndia
  2. 2.Department of Computer Science and Engineering Indian Institute of Technology KharagpurKharagpurIndia

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