A Holistic Approach for Link Prediction in Multiplex Networks

  • Alireza Hajibagheri
  • Gita SukthankarEmail author
  • Kiran Lakkaraju
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)


Networks extracted from social media platforms frequently include multiple types of links that dynamically change over time; these links can be used to represent dyadic interactions such as economic transactions, communications, and shared activities. Organizing this data into a dynamic multiplex network, where each layer is composed of a single edge type linking the same underlying vertices, can reveal interesting cross-layer interaction patterns. In coevolving networks, links in one layer result in an increased probability of other types of links forming between the same node pair. Hence we believe that a holistic approach in which all the layers are simultaneously considered can outperform a factored approach in which link prediction is performed separately in each layer. This paper introduces a comprehensive framework, MLP (Multiplex Link Prediction), in which link existence likelihoods for the target layer are learned from the other network layers. These likelihoods are used to reweight the output of a single layer link prediction method that uses rank aggregation to combine a set of topological metrics. Our experiments show that our reweighting procedure outperforms other methods for fusing information across network layers.


Link Prediction Common Neighbor Unsupervised Method Target Layer Rank Aggregation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. The Travian dataset was provided by Drs. Rolf T. Wigand and Nitin Agarwal (University of Arkansas at Little Rock, Department of Information Science); their research was supported by the National Science Foundation and Travian Games GmbH, Munich, Germany.


  1. 1.
    Hajibagheri, A., Lakkaraju, K., Sukthankar, G., Wigand, R.T., Agarwal, N.: Conflict and communication in massively-multiplayer online games. In: Agarwal, N., Xu, K., Osgood, N. (eds.) SBP 2015. LNCS, vol. 9021, pp. 65–74. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-16268-3_7 Google Scholar
  2. 2.
    Omodei, E., De Domenico, M., Arenas, A.: Characterizing interactions in online social networks during exceptional events. arXiv preprint (2015). arXiv:1506.09115
  3. 3.
    Scott, J.: Social Network Analysis. Sage, London (2012).
  4. 4.
    Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J., Moreno, Y., Porter, M.: Multilayer networks. J. Complex Netw. 2, 203–271 (2014)CrossRefGoogle Scholar
  5. 5.
    Hajibagheri, A., Sukthankar, G., Lakkaraju, K.: Leveraging network dynamics for improved link prediction. In: Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, Washington, D.C., June 2016Google Scholar
  6. 6.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  7. 7.
    Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23783-6_28 CrossRefGoogle Scholar
  8. 8.
    Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054 (2011)Google Scholar
  9. 9.
    Beigi, G., Tang, J., Liu, H.: Signed link analysis in social media networks. arXiv preprint (2016). arXiv:1603.06878
  10. 10.
    Tang, J., Lou, T., Kleinberg, J.: Inferring social ties across heterogenous networks. In: Proceedings of the ACM International Conference on Web Search and Data Mining, pp. 743–752 (2012)Google Scholar
  11. 11.
    Davis, D., Lichtenwalter, R., Chawla, N.V.: Supervised methods for multi-relational link prediction. Soc. Netw. Anal. Min. 3(2), 127–141 (2013)CrossRefGoogle Scholar
  12. 12.
    Hristova, D., Noulas, A., Brown, C., Musolesi, M., Mascolo, C.: A multilayer approach to multiplexity and link prediction in online geo-social networks. arXiv preprint (2015). arXiv:1508.07876
  13. 13.
    Rossetti, G., Berlingerio, M., Giannotti, F.: Scalable link prediction on multidimensional networks. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 979–986. IEEE (2011)Google Scholar
  14. 14.
    Basu, P., Dippel, M., Sundaram, R.: Multiplex networks: A generative model and algorithmic complexity. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 456–463 (2015)Google Scholar
  15. 15.
    Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Phys. A: Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  16. 16.
    Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Zhang, J., Philip, S.Y.: Link prediction across heterogeneous social networks: A survey (2014)Google Scholar
  18. 18.
    Soares, P.R.d.S., Prudêncio, R.B.C.: Time series based link prediction. In: International Joint Conference on Neural Networks, pp. 1–7. IEEE (2012)Google Scholar
  19. 19.
    Gao, S., Denoyer, L., Gallinari, P.: Temporal link prediction by integrating content and structure information. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 1169–1174 (2011)Google Scholar
  20. 20.
    Pujari, M., Kanawati, R.: Supervised rank aggregation approach for link prediction in complex networks. In: Proceedings of the International World Wide Web Conference, pp. 1189–1196 (2012)Google Scholar
  21. 21.
    Pujari, M., Kanawati, R.: Link prediction in multiplex networks. Netw. Heterogen. Media 10(1), 17–35 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Tabourier, L., Bernardes, D.F., Libert, A.S., Lambiotte, R.: Rankmerging: A supervised learning-to-rank framework to predict links in large social network. arXiv preprint (2014). arXiv:1407.2515
  23. 23.
    Wang, X., Sukthankar, G.: Link prediction in heterogeneous collaboration networks. In: Missaoui, R., Sarr, I. (eds.) Social Network Analysis - Community Detection and Evolution. LNCS, pp. 165–192. Springer, Heidelberg (2014)Google Scholar
  24. 24.
    Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 25102 (2001)CrossRefGoogle Scholar
  25. 25.
    Barabási, A.L., et al.: Scale-free networks: a decade and beyond. Science 325(5939), 412 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  27. 27.
    Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)CrossRefzbMATHGoogle Scholar
  28. 28.
    Brin, S., Page, L.: Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)CrossRefGoogle Scholar
  29. 29.
    Ding, Y.: Applying weighted PageRank to author citation networks. J. Am. Soc. Inf. Sci. Technol. 62(2), 236–245 (2011)CrossRefGoogle Scholar
  30. 30.
    Acar, E., Dunlavy, D.M., Kolda, T.G.: Link prediction on evolving data using matrix and tensor factorizations. In: Workshops at IEEE International Conference on Data Mining, pp. 262–269 (2009)Google Scholar
  31. 31.
    Sculley, D.: Rank aggregation for similar items. In: SIAM International Conference on Data Mining, pp. 587–592 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alireza Hajibagheri
    • 1
  • Gita Sukthankar
    • 1
    Email author
  • Kiran Lakkaraju
    • 2
  1. 1.University of Central FloridaOrlandoUSA
  2. 2.Sandia National LabsAlbuquerqueUSA

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