Links in Context: Detecting and Describing the Nested Structure of Communities in Node-Attributed Networks

  • Tobias HeckingEmail author
  • H. Ulrich Hoppe
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


This paper describes Links in Context as a novel approach for detecting and characterising the community structure in networks when further information on the properties of nodes is available. The general idea is straightforward and extends the well-known Link Communities framework introduced by Ahn et al. [1] by additionally taking node attributes into account. The basic assumption is that each edge in a social network emerges in a certain context, which is constituted by the node attributes shared by its two endpoints. In this regard, our approach focuses on subspaces of attributes that are relevant for explaining the emergence of particular edges. The proposed method allows for detecting highly overlapping community structures where nodes can be part of many groups emerging in different social contexts.


Overlapping community detection Attributed networks Link Communities 


  1. 1.
    Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)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), P10,008 (2008)Google Scholar
  3. 3.
    Bothorel, C., Cruz, J.D., Magnani, M., Micenkova, B.: Clustering attributed graphs: models, measures and methods. Netw. Sci. 3(3), 408–444 (2015)Google Scholar
  4. 4.
    Cruz Gomez, J.D., Bothorel, C., Poulet, F.: Semantic clustering of social networks using points of view. In: Proceedings of CORIA: Confrence en Recherche d’Information et Applications 2011 (2011)Google Scholar
  5. 5.
    Díaz Ferreyra, N.E., Hecking, T., Ulrich Hoppe, H., Heisel, M.: Access-control prediction in social network sites: examining the role of homophily. In: Proceedings of the 10th International Conference on Social Informatics, pp. 61–74. Springer International Publishing, Cham (2018)Google Scholar
  6. 6.
    Falih, I.: Attributed network clustering: application to recommender systems. Ph.D. thesis, University Sorbonne Paris Cité (2018)Google Scholar
  7. 7.
    Feld, S.L.: Social structural determinants of similarity among associates. Am. Sociol. Rev. 797–801 (1982)Google Scholar
  8. 8.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)Google Scholar
  9. 9.
    Hric, D., Darst, R.K., Fortunato, S.: Community detection in networks: structural communities versus ground truth. Phys. Rev. E 90(6), 062,805 (2014)Google Scholar
  10. 10.
    Lazarsfeld, P.F., Merton, R.K.: Friendship as a social process: a substantive and methodological analysis. Free. Control. Mod. Soc. 18(1), 18–66 (1954)Google Scholar
  11. 11.
    Lazega, E.: The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership. Oxford University Press (2001)Google Scholar
  12. 12.
    McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, NIPS’12, pp. 539–547. Curran Associates Inc., USA (2012)Google Scholar
  13. 13.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)Google Scholar
  14. 14.
    Misra, G., Such, J.M., Balogun, H.: Non-sharing communities? An empirical study of community detection for access control decisions. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 49–56 (2016)Google Scholar
  15. 15.
    Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: Proceedings of the 13th IEEE International Conference on Data Mining, pp. 1151–1156. IEEE (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Duisburg-EssenDuisburgGermany

Personalised recommendations