Abstract
Given a network where the same set of nodes have multiple types of relationships, how do we efficiently predict potential links in the future (e.g., interactions between social actors), and how do we predict links using information from other relationships? These problems have been widely studied recently, most of the existing methods either aggregate multiple types of relationships into a single network or consider them separately and ignore the correlations across relationships, leading to information loss. In this work, we present TeleLink, a general link prediction model that works for networks with single and multiple relationships. TeleLink predicts potential links based on community detection and improves link prediction by bringing in a cohesive structure across multiple networks constructed by different relationships or node attributes. To further improve the prediction performance, we extend TeleLink to a semi-supervised scheme, incorporating partially labeled information. Our extensive experiments show that TeleLink outperforms existing methods in predicting new links. Specifically, among the various datasets that we study, TeleLink achieves a precision improvement by up to 110 % compared to the baselines.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. JASIST 58(7), 1019–1031 (2007)
Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM 2006: Workshop (2006)
Stumpf, M.P.H., Thomas, T., et al.: Estimating the size of the human interactome. PNAS 105(19), 6959–6964 (2008)
Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 1150–1170 (2011)
Jaccard, P.: Distribution de la Flore Alpine: DANS le Bassin des dranses et dans quelques régions voisines. Rouge (1901)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Feng, X., Zhao, J.C., Xu, K.: Link prediction in complex networks: a clustering perspective. Eur. Phys. J. B 85(1), 1–9 (2012)
Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98–101 (2008)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)
De Domenico, M., Lancichinetti, A., Arenas, A., Rosvall, M.: Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X 5(1), 011027 (2015)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Brin, S., Page, L.: Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)
Backstrom, L., et al.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM WSDM, pp. 635–644. ACM (2011)
Soundarajan, S., Hopcroft, J.: Using community info. to improve the precision of link prediction methods. In: Proceedings of the WWW, pp. 607–608. ACM (2012)
Davis, D., Lichtenwalter, R., Chawla, N.V.: Supervised methods for multi-relational link prediction. Soc. Netw. Anal. Min. 3(2), 127–141 (2013)
Sun, Y., et al.: Co-author relationship prediction in heterogeneous bibliographic networks. In: 2011 International Conference on ASONAM, pp. 121–128. IEEE (2011)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 1118–1123 (2008)
Stehlé, J., et al.: High-resolution measurements of face-to-face contact patterns in a primary school (2011)
Gemmetto, V., Barrat, A., Cattuto, C.: Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect. Dis. 14(1), 695 (2014)
Lin, Y.-R., Keegan, B., Margolin, D., Lazer, D.: Rising tides or rising stars?: Dynamics of shared attention on twitter during media events (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jin, D., Wang, M., Lin, YR. (2016). TeleLink: Link Prediction in Social Network Based on Multiplex Cohesive Structures. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-39931-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39930-0
Online ISBN: 978-3-319-39931-7
eBook Packages: Computer ScienceComputer Science (R0)