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Sampling Community Structure in Dynamic Social Networks

  • Humphrey MensahEmail author
  • Sucheta Soundarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

When studying dynamic networks, it is often of interest to understand how the community structure of the network changes. However, before studying the community structure of dynamic social networks, one must first collect appropriate network data. In this paper we present a network sampling technique to crawl the community structure of dynamic networks when there is a limitation on the number of nodes that can be queried. The process begins by obtaining a sample for the first time step. In subsequent time steps, the crawling process is guided by community structure discoveries made in the past. Experiments conducted on the proposed approach and certain baseline techniques reveal the proposed approach has at least 35% performance increase in cases when the total query budget is fixed over the entire period and at least 8% increase in cases when the query budget is fixed per time step.

References

  1. 1.
    Twitter developer documentation. https://dev.twitter.com/rest/reference/get/followers/ids. Accessed 16 Aug 2017
  2. 2.
    Alvari, H., Hajibagheri, A., Sukthankar, G., Lakkaraju, K.: Identifying community structures in dynamic networks. Soc. Netw. Anal. Min. 6(1), 77 (2016)CrossRefGoogle Scholar
  3. 3.
    Bedi, P., Sharma, C.: Community detection in social networks. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 6(3), 115–135 (2016)Google Scholar
  4. 4.
    Benyahia, O., Largeron, C., Jeudy, B., Zaïane, O.R.: DANCer: dynamic attributed network with community structure generator. In: Berendt, B., Bringmann, B., Fromont, É., Garriga, G., Miettinen, P., Tatti, N., Tresp, V. (eds.) ECML PKDD 2016, Part III. LNCS (LNAI), vol. 9853, pp. 41–44. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46131-1_9CrossRefGoogle Scholar
  5. 5.
    Blenn, N., Doerr, C., Van Kester, B., Van Mieghem, P.: Crawling and detecting community structure in online social networks using local information. In: Bestak, R., Kencl, L., Li, L.E., Widmer, J., Yin, H. (eds.) NETWORKING 2012. LNCS, vol. 7289, pp. 56–67. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-30045-5_5CrossRefGoogle Scholar
  6. 6.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theor. Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  7. 7.
    Chen, Y., Qiu, X.: Detecting community structures in social networks with particle swarm optimization. In: Su, J., Zhao, B., Sun, Z., Wang, X., Wang, F., Xu, K. (eds.) Frontiers in Internet Technologies. CCIS, vol. 401, pp. 266–275. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-53959-6_24CrossRefGoogle Scholar
  8. 8.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)CrossRefGoogle Scholar
  9. 9.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187. ACM (2005)Google Scholar
  10. 10.
    Lin, Y.-R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th International Conference on World Wide Web, pp. 685–694. ACM (2008)Google Scholar
  11. 11.
    Lu, X., Phan, T.Q., Bressan, S.: Incremental algorithms for sampling dynamic graphs. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013, Part I. LNCS, vol. 8055, pp. 327–341. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40285-2_29CrossRefGoogle Scholar
  12. 12.
    Maiya, A.S., Berger-Wolf, T.Y.: Sampling community structure. In: Proceedings of the 19th International Conference on World Wide Web, pp. 701–710. ACM (2010)Google Scholar
  13. 13.
    Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: Proceedings of the 2011 IEEE International Conference on Computer Communications, pp. 2282–2290. IEEE (2011)Google Scholar
  14. 14.
    Salehi, M., Rabiee, H.R., Rajabi, A.: Sampling from complex networks with high community structures. Chaos: Interdisc. J. Nonlinear Sci. 22(2), 023126 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: Graphscope: parameter-free mining of large time-evolving graphs. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 687–696. ACM (2007)Google Scholar
  16. 16.
    Thakur, G.S., Tiwari, R., Thai, M.T., Chen, S.-S., Dress, A.W.M.: Detection of local community structures in complex dynamic networks with random walks. IET Syst. Biol. 3(4), 266–278 (2009)CrossRefGoogle Scholar
  17. 17.
    Wang, C.-D., Lai, J.-H., Philip, S.Y.: Neiwalk: community discovery in dynamic content-based networks. IEEE Trans. Knowl. Data Eng. 26(7), 1734–1748 (2014)CrossRefGoogle Scholar
  18. 18.
    Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1151–1156. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA

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