Detecting cohesive and 2-mode communities indirected and undirected networks

ABSTRACT

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks.

In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.

References

1. L. Adamic and N. Glance. The political blogosphere and the 2004 U.S. election: divided they blog. In LinkKDD '05, 2005.
2. Y.-Y. Ahn, J. Bagrow, and S. Lehmann. Link communities reveal multi-scale complexity in networks. Nature, 2010.
3. E. Airoldi, D. Blei, S. Fienberg, and E. Xing. Mixed membership stochastic blockmodels. JMLR, 2007.
4. R. Andersen and K. Lang. Communities from seed sets. In WWW '06, 2006.
5. R. Balasubramanyan and W. Cohen. Block-LDA: Jointly modeling entity-annotated text and entity-entity links. In SDM '11, 2011.
6. B. Ball, B. Karrer, and M. Newman. Efficient and principled method for detecting communities in networks. In Phys. Rev. E, 2011.
7. E. Boyle et al. GO::TermFinder--open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics, 2004.
8. L. Breiger. The duality of persons and groups. Social Forces, 1974.
9. R. Burt. Cohesion versus structural equivalence as a basis for network subgroups. Sociological Methods and Research, 1978.
10. D. Carney, B. Davies, and B. Horazdovsky. Vps9 domain-containing proteins: activators of Rab5 GTPases from yeast to neurons. Trends in Cell Biology, 2006.
11. M. Coscia, G. Rossetti, F. Giannotti, and D. Pedreschi. Demon: a local-first discovery method for overlapping communities. In KDD '12, 2012.
12. L. Danon, J. Duch, A. Diaz-Guilera, and A. Arenas. Comparing community structure identification. J. of Stat. Mech.: Theory and Experiment, 2005.
13. I. Dhillon, Y. Guan, and B. Kulis. Weighted graph cuts without eigenvectors: A multilevel approach. IEEE PAMI, 2007.
14. S. Fortunato. Community detection in graphs. Physics Reports, 2010.
15. D. Gleich and Seshadhri. Neighborhoods are good communities. In KDD '12, 2012.
16. P. Gopalan, D. Mimno, S. Gerrish, M. Freedman, and D. Blei. Scalable inference of overlapping communities. In NIPS '12, 2012.
17. K. Henderson, B. Gallagher, T. Eliassi-Rad, H. Tong, S. Basu, L. Akoglu, D. Koutra, C. Faloutsos, and L. Li. Rolx: structural role extraction & mining in large graphs. In KDD, 2012.
18. P. W. Holland, K. B. Laskey, and S. Leinhardt. Stochastic blockmodels: First steps. Social Networks, 1983.
19. C.-J. Hsieh and I. S. Dhillon. Fast coordinate descent methods with variable selection for non-negative matrix factorization. In KDD '11, 2011.
20. S. Johnson. Hierarchical clustering schemes. Psychometrika, 1967.
21. G. Karypis and V. Kumar. Multilevel k-way partitioning scheme for irregular graphs. J. of Parallel and Distributed Computing, 1998.
22. B. Klimt and Y. Yang. Introducing the enron corpus. In CEAS '04, 2004.
23. R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for emerging cyber-communities. Computer Networks, 1999.
24. H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In WWW '10, 2010.
25. S. Lattanzi and D. Sivakumar. Affiliation networks. In STOC '09, 2009.
26. E. Leicht and M. E. Newman. Community structure in directed networks. Phys. Rev. Lett., 2008.
27. J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time: densification laws, shrinking diameters and possible explanations. In KDD '05, 2005.
28. J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics, 2009.
29. C-J. Lin. Projected Gradient Methods for Nonnegative Matrix Factorization. Neural Computation, 2007.
30. F. Malliaros and M. Vazirgiannis. Clustering and Community Detection in Directed Networks: A Survey. Physics Reports, 2013.
31. J. McAuley and J. Leskovec. Learning to discover social circles in ego networks. In NIPS '12, 2012.
32. A. McCallum, X. Wang, and A. Corrada-Emmanuel. Topic and role discovery in social networks with experiments on enron and academic email. JAIR, 2007.
33. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In IMC '07, 2007.
34. G. Palla, I. Derényi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 2005.
35. S. Papadopoulos, Y. Kompatsiaris, A. Vakali, and P. Spyridonos. Community detection in social media. DMKD, 2011.
36. S. Pinkert, J. Schultz, and J. Reichardt. Protein interaction networks---more than mere modules. PLoS CompBio, 2010.
37. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi. Defining and identifying communities in networks. PNAS, 2004.
38. M. Rosvall and C. Bergstrom. An information-theoretic framework for resolving community structure in complex networks. PNAS, 2007.
39. M. Rosvall and C. T. Bergstrom. Maps of random walks on complex networks reveal community structure. PNAS, 2008.
40. V. Satuluri and S. Parthasarathy. Scalable Graph Clustering using Stochastic Flows: Applications to Community Discovery. KDD '09, 2009.
41. R. Ulanowicz, C. Bondavalli, and M. Egnotovich. Network analysis of trophic dynamics in south florida ecosystem, fy 97: The florida bay ecosystem. Ref. CBL98-123. Chesapeake Biological Laboratory, 1998.
42. J. Xie, S. Kelley, and B. K. Szymanski. Overlapping community detection in networks: the state of the art and comparative study. ACM Computing Surveys, 2013.
43. J. Yang and J. Leskovec. Community-affiliation network model for overlapping community detection. In ICDM '12, 2012.
44. J. Yang and J. Leskovec. Defining and evaluating network communities based on ground-truth. In ICDM '12, 2012.
45. J. Yang and J. Leskovec. Overlapping community detection at scale: A non-negative factorization approach. In WSDM '13, 2013.
46. H. Yu, P. Braun, M. A. Yildirim, I. Lemmens, K. Venkatesan, J. Sahalie, T. Hirozane-Kishikawa, F. Gebreab, N. Li, N. Simonis, and et al. High-quality binary protein interaction map of the yeast interactome network. Science, 2008.
47. E. Zheleva, H. Sharara, and L. Getoor. Co-evolution of social and affiliation networks. In KDD '09, 2009.

Index Terms

• Information systems

  • Information systems applications

    • Data mining