Abstract
People regularly attend various social events to interact with other community members. For example, researchers attend conferences to present their work and to network with other researchers. In this paper, we propose an E vent-based COmmunity DEtection algorithm ECODE to mine the underlying community substructures of social networks from event information. Unlike conventional approaches, ECODE makes use of content similarity-based virtual links which are found to be more useful for community detection than the physical links. By performing partial computation between an event and its candidate relevant set instead of computing pair-wise similarities between all the events, ECODE is able to achieve significant computational speedup. Extensive experimental results and comparisons with other existing methods showed that our ECODE algorithm is both efficient and effective in detecting communities from social networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albert, R., Jeong, H., Barabási, A.-L.: Diameter of the world-wide web. Nature 401, 130–131 (1999)
Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)
Li, X.-L., et al.: Searching for Rising Stars in Bibliography Networks. In: DASFAA (2009)
Palla, G., et al.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Li, X.-L., et al.: Interaction Graph Mining for Protein Complexes Using Local Clique Merging. Genome Informatics 16(2) (2005)
Li, X.-L., Foo, C.-S., Ng, S.-K.: Discovering Protein Complexes in Dense Reliable Neighborhoods of Protein Interaction Networks. In: CSB (2007)
Steinhaeuser, K., Chawla, N.: A Network-Based Approach to Understanding and Predicting Diseases. Springer, Heidelberg (2009)
Wu, M., et al.: A Core-Attachment based Method to Detect Protein Complexes in PPI Networks. BMC Bioinformatics 10(169) (2009)
Li, X.-L., et al.: Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genomics 11(Suppl 1:S3) (2010)
Redner, S.: How popular is your paper? An Empirical Study of the Citation Distribution. Eur. Phys. J. B(4), 131–138 (1998)
Nisheeth, S., Anirban, M., Rastogi, R.: Mining (Social) Network Graphs to Detect Random Link Attacks. In: ICDE (2008)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. EÂ 70, 066111 (2004)
Radicchi, F., et al.: Defining and identifying communities in networks. PNAS 101(9), 2658–2663 (2004)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)
Ravasz, E., et al.: Hierarchical Organization of Modularity in Metabolic Networks. Science 297, 1551–1555 (2002)
Clauset, A.: Finding local community structure in networks. Phys. Rev. EÂ 72 (2005)
Boccaletti, S., et al.: Detection of Complex Networks Modularity by Dynamical Clustering. Physical Review E, 75 (2007)
Shen, H., et al.: Detect overlapping and hierarchical community structure in networks. CoRR abs/0810.3093 (2008)
Seidman, S.B.: Network structure and minimum degree. Social Networks 5, 269–287 (1983)
Holme, P., Huss, M., Jeong, H.: Subnetwork hierarchies of biochemical pathways. Bioinformatics 19(4), 532–538 (2003)
Gleiser, P., Danon, L.: Community structure in jazz. Advances in Complex Systems 6, 565 (2003)
Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as Spectroscopy: Automated Discovery of Community Structure within Organizations. Communities and Technologies, 81–96 (2003)
Gregory, S.: A fast algorithm to find overlapping communities in networks. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 408–423. Springer, Heidelberg (2008)
Bie, T.D., Cristianini, N.: Fast SDP relaxations of graph cut clustering, transduction, and other combinatorial problems. Journal of Machine Learning Research 7, 1409–1436 (2006)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)
Newman, M.E.J.: Detecting community structure in networks. European Physical Journal B 38, 321–330 (2004)
Ding, C., He, X., Zha, H.: A Spectral Method to Separate Disconnected and Nearly-disconnected Web Graph Components. In: KDD (2001)
Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994)
Backstrom, L., et al.: Group Formation in Large Social Networks: Membership, Growth, and Evolution. In: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, USA (2006)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD (2005)
Kimura, M., Saito, K.: Tractable Models for Information Diffusion in Social Networks. In: ECML/PKDD (2006)
Tang, L., et al.: Community Evolution in Dynamic Multi-Mode Networks. In: SIGKDD (2008)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: SIGMOD Conference (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, XL., Tan, A., Yu, P.S., Ng, SK. (2011). ECODE: Event-Based Community Detection from Social Networks. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-20149-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20148-6
Online ISBN: 978-3-642-20149-3
eBook Packages: Computer ScienceComputer Science (R0)