Abstract
Social networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Discovering the overlapping and hierarchical community structure of a social network can provide researchers a deeper understanding of the social network. In this paper, we define the overlapping and hierarchical community as a hierarchy presenting overlapping communities of a social network at different levels of granularity. We propose an algorithm DOHACS to derive overlapping and hierarchical communities from a social network which learn Gaussian mixture models from the social network at various granularities, and then organizing the overlapping communities into a hierarchy. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the proposed algorithm.
This work was supported by the Humanities and Social Science Foundation for the Youth Scholars of Ministry of Education of China (No. 09YJCZH101).
This is a preview of subscription content, log in via an institution to check access.
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
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: A Structural Clustering Algorithm for Networks. In: The Proceeding of SIGKDD 2007 (2007)
Huang, J., Sun, H., Han, J., Deng, H., Sun, Y., Liu, Y.: SHRINK: A Structural Clustering Algorithm for Detecting Hierarchical Communities in Networks. In: The Proceedings of CIKM 2010 (2010)
Craswell, N., Szummer, M.: Random walks on the click graph. In: The Proceedings of the 30th Annual International ACM SIGIR Conference, pp. 239–246 (2007)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Shen, H., Cheng, X., Cai, K., Hu, M.-B.: Detect overlapping and hierarchical community structure in networks. Physica A: Statistical Mechanics and its Applications 388(8) (2009)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review EÂ 69, 066133 (2004)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11(3) (2009)
Gregory, S.: Finding Overlapping Communities Using Disjoint Community Detection Algorithms. In: Fortunato, S., Mangioni, G., Menezes, R. (eds.) Complex Networks: CompleNet 2009. SCI, vol. 207, pp. 47–61. Springer, Heidelberg (2009)
Leicht, E.A., Clarkson, G., Shedden, K., Newman, M.E.J.: Large-scale structure of time evolving citation networks. The European Physical Journal BÂ 59(1) (2007)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113, 1–15 (2004)
Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)
Shen, H.-W., Cheng, X.-Q., Guo, J.-F.: Quantifying and identifying the overlapping community structure in networks. Journal of Statistical Mechanics: Theory and Experiment (7) (2009)
Gregory, S.: Fuzzy overlapping communities in networks. Journal of Statistical Mechanics: Theory and Experiment 2011(2) (2011)
Chekuri, C., Goldberg, A., Karger, D., Levin, M., Stein, C.: Experimentao study of minimum cut algorithms. In: Proc. 8th ACM-SAIM Symposium on Discreet Algorithm, pp. 324–333 (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
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qiu, J., Hu, Y. (2013). Discovering the Overlapping and Hierarchical Community Structure in a Social Network. In: Wang, M. (eds) Knowledge Science, Engineering and Management. KSEM 2013. Lecture Notes in Computer Science(), vol 8041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39787-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-39787-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39786-8
Online ISBN: 978-3-642-39787-5
eBook Packages: Computer ScienceComputer Science (R0)