Abstract
Community structure reveals hidden information about networks which cannot be discerned using other topological properties. The prevalent community detection algorithms, such as the Girvan-Newman algorithm, utilize a centralized approach which offers poor performance. We propose a self-organized approach to community detection which utilizes the newly introduced concept of node entropy to allow individual nodes to make decentralized decisions concerning the community to which they belong. Node entropy is a mathematical expression of an individual node’s satisfaction with its current community. As nodes become more “satisfied”, i.e., entropy is low, the community structure of a network is emergent. Our algorithm offers several advantages over existing algorithms including near-linear performance, identification of community overlaps, and localized management of dynamic changes in the network.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media (2009)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (10), P10008+ (2008)
Cruz, J.D., Bothorel, C., Poulet, F.: Entropy based community detection in augmented social networks. In: CASoN, pp. 163–168. IEEE (2011)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Fortunato, S., Barthélemy, M.: Resolution limit in community detection. PNAS 104, 36 (2007)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)
Kleinberg, J.: The small-world phenomenon: an algorithm perspective. In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, STOC 2000, pp. 163–170. ACM, New York (2000)
Ma, X., Gao, L., Yong, X.: Eigenspaces of networks reveal the overlapping and hierarchical community structure more precisely. Journal of Statistical Mechanics: Theory and Experiment (08), P08012 (2010)
Mamei, M., Menezes, R., Tolksdorf, R., Zambonelli, F.: Case studies for self-organization in computer science. J. Syst. Archit. 52(8), 443–460 (2006)
Newman, M.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)
Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.S.: Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recogn. 43(1), 113–127 (2010)
Reichardt, J., Bornholdt, S.: Detecting fuzzy community structures in complex networks with a potts model. Physical Review Letters 93(21), 218701 (2004)
Shannon, C., Petigara, N., Seshasai, S.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423 (1948)
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 1997), IEEE Computer Society, Washington, DC (1997)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Collingsworth, B., Menezes, R. (2013). A Self-organized Approach for Detecting Communities in Networks. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds) Intelligent Distributed Computing VI. Studies in Computational Intelligence, vol 446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32524-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-32524-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32523-6
Online ISBN: 978-3-642-32524-3
eBook Packages: EngineeringEngineering (R0)