Abstract
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.
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
References
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)
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)
Bothorel, C., Cruz, J.D., Magnani, M., Micenkova, B.: Clustering attributed graphs: models, measures and methods. Netw. Sci. 3(3), 408–444 (2015)
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)
DÃaz 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)
Falih, I.: Attributed network clustering: application to recommender systems. Ph.D. thesis, University Sorbonne Paris Cité (2018)
Feld, S.L.: Social structural determinants of similarity among associates. Am. Sociol. Rev. 797–801 (1982)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Hric, D., Darst, R.K., Fortunato, S.: Community detection in networks: structural communities versus ground truth. Phys. Rev. E 90(6), 062,805 (2014)
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)
Lazega, E.: The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership. Oxford University Press (2001)
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)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hecking, T., Ulrich Hoppe, H. (2019). Links in Context: Detecting and Describing the Nested Structure of Communities in Node-Attributed Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-05411-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05410-6
Online ISBN: 978-3-030-05411-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)