Advertisement

Eva: Attribute-Aware Network Segmentation

  • Salvatore Citraro
  • Giulio RossettiEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

Abstract

Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.

Notes

Acknowledgment

This work is partially supported by the European Community’s H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement 654024, http://www.sobigdata.eu, “SoBigData”.

References

  1. 1.
    MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)Google Scholar
  2. 2.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  3. 3.
    McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3, 127–163 (2000)CrossRefGoogle Scholar
  4. 4.
    Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)Google Scholar
  5. 5.
    Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 625–630. ACM (2003)Google Scholar
  6. 6.
    Trask, A., Michalak, P., Liu, J.: sense2vec - a fast and accurate method for word sense disambiguation in neural word embeddings. CoRR, vol. abs/1511.06388 (2015)Google Scholar
  7. 7.
    Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of Facebook networks. CoRR, vol. abs/1102.2166 (2011)Google Scholar
  8. 8.
    Traag, V.A., Waltman, L., van Eck, N.J.: From louvain to leiden: guaranteeing well-connected communities. CoRR, vol. abs/1810.08473 (2018)Google Scholar
  9. 9.
    Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36–41 (2007)CrossRefGoogle Scholar
  10. 10.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  11. 11.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)CrossRefGoogle Scholar
  12. 12.
    Dang, T.A., Viennet, E.: Community detection based on structural andattribute similarities. In: International Conference on Digital Society (ICDS) (2012)Google Scholar
  13. 13.
    Falih, I., Grozavu, N., Kanawati, R., Bennani, Y.: Community detection in attributed network. In: Companion Proceedings of the The Web Conference, pp. 1299–1306 (2018)Google Scholar
  14. 14.
    Neville, J., Adler, M., Jensen, D.: Clustering relational data using attribute and link information. In: 18th International Joint Conference on Artificial Intelligence, pp. 9–15 (2003)Google Scholar
  15. 15.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2, 718–729 (2009)CrossRefGoogle Scholar
  16. 16.
    Combe, D., Largeron, C., Géry, M., Egyed-Zsigmond, E.: I-Louvain: an attributed graph clustering method. In: Advances in Intelligent Data Analysis XIV, pp. 181–192. Springer, Cham (2015)CrossRefGoogle Scholar
  17. 17.
    Falih, I., Grozavu, N., Kanawati, R., Bennani, Y.: ANCA : attributed network clustering algorithm. In: Complex Networks and Their Applications, vol. VI, pp. 241–252. Springer, Cham (2018)Google Scholar
  18. 18.
    Elhadi, H., Agam, G.: Structure and attributes community detection: comparative analysis of composite, ensemble and selection methods. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNAKDD 2013, pp. 10:1–10:7. ACM (2013)Google Scholar
  19. 19.
    Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1151–1156, December 2013Google Scholar
  20. 20.
    Rossetti, G., Milli, L., Cazabet, R.: CDLIB: a python library to extract, compare and evaluate communities from complex networks. Appl. Netw. Sci. 4(1), 52 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.KDD LabISTI-CNRPisaItaly

Personalised recommendations