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Searching overlapping communities for group query

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Abstract

In most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. Thus, overlapping community discovery has drawn a great deal of attention and there is a lot of research on it. However, most work has focused on community detection, which takes the whole network as input and derives all communities at one time. Community detection can only be used in offline analysis of networks and it is quite costly, not flexible and can not support dynamically evolving networks. Online community search which only finds overlapping communities containing a given node is a flexible and light-weight solution, and also supports dynamic graphs very well. However, in some scenarios, it requires overlapping community search for group query, which means that the input is a set of nodes instead of one single node. To solve this problem, we propose an overlapping community search framework for group query, including both exact and heuristic solutions. The heuristic solution has four strategies, some of which are adjustable and self-adaptive. We propose two parameters node degree and discovery power to trade off the efficiency and quality of the heuristic strategies, in order to make them satisfy different application requirements. Comprehensive experiments are conducted and demonstrate the efficiency and quality of both exact and heuristic solutions.

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Notes

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    http://dblp.uni-trier.de/xml/

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    http://snap.stanford.edu/data/

References

  1. 1.

    Adamcsek, B., Palla, G., Farkas, I.J., Derényi, I., Vicsek, T.: Cfinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8), 1021–1023 (2006)

  2. 2.

    Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)

  3. 3.

    Bagrow, J.P.: Evaluating local community methods in networks. J. Stat. Mech. Theory Exp. 2008(05), P05001 (2008)

  4. 4.

    Bagrow, J.P., Bollt, E.M.: Local method for detecting communities. Phys. Rev. E 72(4), 046108 (2005)

  5. 5.

    Brunato, M., Hoos, H., Battiti, R.: On effectively finding maximal quasi-cliques in graphs. In: Learning and Intelligent Optimization, pp 41–55. Springer, Berlin (2008)

  6. 6.

    Chen, J., Zaïane, O., Goebel, R.: Local community identification in social networks. In: International Conference on Advances in Social Network Analysis and Mining, 2009. ASONAM’09, pp 237–242. IEEE (2009)

  7. 7.

    Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72 (2), 026132 (2005)

  8. 8.

    Cui, W., Xiao, Y., Wang, H., Lu, Y., Wang, W.: Online search of overlapping communities. In: Proceedings of the 2013 International Conference on Management of Data, pp 277–288. ACM (2013)

  9. 9.

    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 (2007)

  10. 10.

    Evans, T., Lambiotte, R.: Line graphs, link partitions, and overlapping communities. Phys. Rev. E 80(1), 016105 (2009)

  11. 11.

    Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 150–160. ACM (2000)

  12. 12.

    Gibson, D., Kumar, R., Tomkins, A.: Discovering large dense subgraphs in massive graphs. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp 721–732. VLDB Endowment (2005)

  13. 13.

    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)

  14. 14.

    Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)

  15. 15.

    Herrera, M., Roberts, D.C., Gulbahce, N.: Mapping the evolution of scientific fields. PloS one 5(5), e10355 (2010)

  16. 16.

    Jonsson, P.F., Cavanna, T., Zicha, D., Bates, P.A.: Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis. BMC Bioinformatics 7(1), 2 (2006)

  17. 17.

    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: Proceedings of the 17th International Conference on World Wide Web, pp 695–704. ACM (2008)

  18. 18.

    Lim, S., Ryu, S., Kwon, S., Jung, K., Lee, J.G.: Linkscan*: Overlapping community detection using the link-space transformation. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp 292–303. IEEE (2014)

  19. 19.

    Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. Web Intelligence and Agent Systems 6(4), 387–400 (2008)

  20. 20.

    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)

  21. 21.

    Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.: Community detection in social media. Data Min. Knowl. Disc. 24(3), 515–554 (2012)

  22. 22.

    Papadopoulos, S., Skusa, A., Vakali, A., Kompatsiaris, Y., Wagner, N.: Bridge bounding: a local approach for efficient community discovery in complex networks (2009). arXiv:0902.0871

  23. 23.

    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)

  24. 24.

    Shan, J., Shen, D., Nie, T., Kou, Y., Yu, G.: An efficient approach of overlapping communities search. In: Database Systems for Advanced Applications, pp 374–388. Springer (2015)

  25. 25.

    Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 939–948. ACM (2010)

  26. 26.

    Šubelj, L., Bajec, M.: Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction. Phys. Rev. E 83(3), 036103 (2011)

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Acknowledgments

This work is supported by the National Basic Research 973 Program of China under Grant No.2012CB316201, the National Natural Science Foundation of China under Grant Nos. 61033007, 61472070, and the Fundamental Research Funds for the Central Universities (N120816001).

Author information

Correspondence to Jing Shan.

Additional information

This paper is an extended version of our previous conference paper [24].

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Shan, J., Shen, D., Nie, T. et al. Searching overlapping communities for group query. World Wide Web 19, 1179–1202 (2016). https://doi.org/10.1007/s11280-015-0378-5

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Keywords

  • Community search
  • Social networks
  • Graph mining