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Link Prediction via Multi-hashing Framework

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2016)


Link prediction is crucial in various real world applications such as social network analysis and recommendation systems. For example, in social networks, where social actors and their ties (friendship or collaboration) are represented as nodes and links, link prediction can help anticipate future social tie formation. This problem has generally been tackled through computing a “similarity” – measured through graph topological structure or various node attributes and relationships among them (e.g. researcher’s affiliation or research interest). However, when considering multiple relationships, existing link prediction methods often ignored that similarities across different relationships may be “non-transitive”, i.e., they are not necessarily consistent with each other. Here, we develop a semi-supervised link prediction method via a Multi-Component Hashing framework. We derive multiple hashing tables for nodes in a network with each hash table corresponding to a particular type of non-transitive similarity aspect such as prior collaboration experience or topical interest. New links are predicted based on whether nodes are closer in the hashing tables. Results on three co-authorship networks show that our approach outperforms the state-of-the-art unsupervised and supervised methods. The results also show the superiority of our method in cold-start link prediction setting, where no or little knowledge about the nodes’ network positions is given in the training phase.

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  1. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)

    Article  Google Scholar 

  2. Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 635–644. ACM (2011)

    Google Scholar 

  3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  4. Gao, S., Denoyer, L., Gallinari, P.: Tensor decomposition model for link prediction in multi-relational networks. In: 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content, pp. 298–302. IEEE (2010)

    Google Scholar 

  5. Han, S., He, D., Brusilovsky, P., Yue, Z.: Coauthor prediction for junior researchers. In: Social Computing, Behavioral-Cultural Modeling and Prediction, pp. 274–283 (2013)

    Google Scholar 

  6. Jaccard, P.: Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr. Corbaz (1901)

    Google Scholar 

  7. Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD, pp. 538–543. ACM (2002)

    Google Scholar 

  8. Leroy, V., Cambazoglu, B.B., Bonchi, F.: Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD, pp. 393–402. ACM (2010)

    Google Scholar 

  9. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)

    Article  Google Scholar 

  12. Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  13. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  14. Ou, M., Cui, P., Wang, F., Wang, J., Zhu, W.: Non-transitive hashing with latent similarity components. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 895–904. ACM (2015)

    Google Scholar 

  15. Sarkar, P., Chakrabarti, D., Jordan, M.: Nonparametric link prediction in dynamic networks, arXiv preprint arXiv:1206.6394 (2012)

  16. Stumpf, M.P., Thorne, T., de Silva, E., Stewart, R., An, H.J., Lappe, M., Wiuf, C.: Estimating the size of the human interactome. PNAS 105(19), 6959–6964 (2008)

    Article  Google Scholar 

  17. Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  18. Szell, M., Lambiotte, R., Thurner, S.: Multirelational organization of large-scale social networks in an online world. PNAS 107(31), 13636–13641 (2010)

    Article  Google Scholar 

  19. Tsai, C.-H., Lin, Y.-R.: The evolution of scientific productivity of junior scholars. In: International Conference 2015 Proceedings (2015)

    Google Scholar 

  20. Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)

    Article  Google Scholar 

  21. Wang, J., Shen, H.T., Song, J., Ji, J.: Hashing for similarity search: A survey, arXiv preprint arXiv:1408.2927 (2014)

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Correspondence to Mengdi Wang .

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Wang, M., Lin, YR. (2016). Link Prediction via Multi-hashing Framework. 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.

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  • Print ISBN: 978-3-319-39930-0

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