Abstract
To identify global community structure in networks is a great challenge that requires complete information of graphs, which is not feasible for some large networks, e.g. the World Wide Web. Recently, local algorithms have been proposed to extract communities in nearly linear time, which just require a small part of the graphs. However, their results, largely depending on the starting vertex, are not stable. In this paper, we propose a local modularity method for extracting local communities from local cores instead of random vertices. This approach firstly extracts a large enough local core with a heuristic strategy. Then, it detects the corresponding local community by optimizing local modularity, and finally removes outliers based on introversion. Experiment results indicate that, compared with previous algorithms, our method can extract stable meaningful communities with higher quality.
This work was partially supported by NSFC under grant No. 60873180, 61070016, SRF for ROCS, State Education Ministry, and by the Fundamental Research Funds (DUT10JR02) for the Central Universities, China.
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
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On Power-Law Relationships of the Internet Topology. In: Proceedings of Annual Conference of the Special Interest Group on Data Communication (SIGCOMM 1999), pp. 251–262. ACM, New York (1999)
Albert, R., Jeong, H., Barabsi, A.L.: Diameter of the World-Wide Web. Nature (401), 130–131 (1999)
Hajra, K.B., Sen, P.: Aging in Citation Networks. Physica A, 44–48 (2005)
Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, New York (2003)
Newman, M.E.J.: Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences 103, 8577–8582 (2006)
Dourisboure, Y., Geraci, F., Pellegrini, M.: Extraction and Classification of Dense Communities in the Web. In: Proceedings of the 16th International Conference on World Wide Web, pp. 461–470. ACM, New York (2007)
Andersen, R.: A Local Algorithm for Finding Dense Subgraphs. ACM Transactions on Algorithms (TALG) 6, 1–12 (2010)
Zhang, X., Li, Y., Liang, W.: C&C: An effective algorithm for extracting web community cores. In: Yoshikawa, M., Meng, X., Yumoto, T., Ma, Q., Sun, L., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 6193, pp. 316–326. Springer, Heidelberg (2010)
Gibson, D., Kleinberg, J., Raghavan, P.: Inferring Web Communities from Link Topology. In: Proceedings of the 9th ACM Conference on Hypertext and Hypermedia: Links, Objects, Time and Space, pp. 225–234. ACM, New York (1998)
Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. Physical Review EÂ 69, 26113 (2004)
Andersen, R., Lang, K.J.: An Algorithm for Improving Graph Partitions. In: Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 651–660 (2008)
Sharan, A., Gupta, S.L.: Identification of Web Communities through Link Based Approaches. In: International Conference on Information Management and Engineering, pp. 703–708 (2009)
Flake, G.W., Lawrence, S., Giles, C.L.: Efficient Identification of Web Communities. In: Proceedings of the 6th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 150–160 (2000)
Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self- organization and Identification of Web Communities. Computer 35, 66–70 (2002)
Khandekar, R., Rao, S., Vazirani, U.: Graph Partitioning using Commodity Flows. Journal of the ACM (JACM) 56, 1–15 (2009)
Newman, M.E.J.: Fast Algorithm for Detecting Community Structure in Networks. Physical Review EÂ 69, 26133 (2004)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment (2008)
Clauset, A.: Finding Local Community Structure in Networks. Physical Review EÂ 72(2), 26132 (2005)
Luo, F., Wang, J.Z., Promislow, E.: Exploring Local Community Structures in Large Networks. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 233–239 (2006)
Bagrow, J.P.: Evaluating Local Community Methods in Networks. Journal of Statistical Mechanics: Theory and Experiment 2008, P5001 (2008)
Andersen, R.: A Local Algorithm for Finding Dense Subgraphs. In: Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1003–1009 (2008)
Chen, J., Zaizne, O., Goebel, R.: Local Community Identification in Social Networks. In: International Conference on Advances in Social Network Analysis and Mining, pp. 237–242. IEEE, Los Alamitos (2009)
Muff, S., Rao, F., Caflisch, A.: Local Modularity Measure for Network Clusterizations. Physical Review EÂ 72(5), 56107 (2005)
Hinne, M.: Local Identification of Web Graph Communities. In: Proceedings of the 1st International Conference on Theory of Information Retrieval, pp. 261–278 (2007)
Schaeffer, S.E.: Graph Clustering. Computer Science Review 1(1), 27–64 (2007)
Zachary, W.W.: An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research 33(4), 452–473 (1977)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: Scan: A Structural Clustering Algorithm for Networks. In: KDD, pp. 824–833 (2007)
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
Zhang, X., Wang, L., Li, Y., Liang, W. (2011). Extracting Local Community Structure from Local Cores. In: Xu, J., Yu, G., Zhou, S., Unland, R. (eds) Database Systems for Adanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20244-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-20244-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20243-8
Online ISBN: 978-3-642-20244-5
eBook Packages: Computer ScienceComputer Science (R0)