Abstract
The aim of link prediction is to forecast connections that are most likely to occur in the future, based on examples of previously observed links. A key insight is that it is useful to explicitly model network dynamics, how frequently links are created or destroyed when doing link prediction. In this paper, we introduce a new supervised link prediction framework, RPM (Rate Prediction Model). In addition to network similarity measures, RPM uses the predicted rate of link modifications, modeled using time series data; it is implemented in Spark-ML and trained with the original link distribution, rather than a small balanced subset. We compare the use of this network dynamics model to directly creating time series of network similarity measures. Our experiments show that RPM, which leverages predicted rates, outperforms the use of network similarity measures, either individually or within a time series.
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References
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)
Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explor. Newslett. 7(2), 3–12 (2005)
Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: IEEE International Conference on Data Mining, pp. 322–331 (2007)
Huang, Z., Lin, D.K.: The time-series link prediction problem with applications in communication surveillance. INFORMS J. Comput. 21(2), 286–303 (2009)
Berlingerio, M., Bonchi, F., Bringmann, B., Gionis, A.: Mining graph evolution rules. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS, vol. 5781, pp. 115–130. Springer, Heidelberg (2009)
Potgieter, A., April, K.A., Cooke, R.J., Osunmakinde, I.O.: Temporality in link prediction: Understanding social complexity. Emergence Complex. Organ. (E: CO) 11(1), 69–83 (2009)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the International Conference on Information and Knowledge Management, pp. 556–559 (2003)
Soares, P.R.D.S., Prudêncio, R.B.C.: Time series based link prediction. In: International Joint Conference on Neural Networks, IEEE, pp. 1–7 (2012)
Cohen, W.W.: Enron email dataset (2009). http://www.cs.cmu.edu/enron/
Hajibagheri, A., Lakkaraju, K., Sukthankar, G., Wigand, R.T., Agarwal, N.: Conflict and communication in massively-multiplayer online games. In: Agarwal, N., Kevin, X., Osgood, N. (eds.) Social Computing, Behavioral-Cultural Modeling, and Prediction. LNCS, pp. 65–74. Springer, Heidelberg (2015)
Wang, X., Sukthankar, G.: Link prediction in heterogeneous collaboration networks. In: Missaoui, R., Sarr, I. (eds.) Social Network Analysis: Community Detection and Evolution. Lecture Notes in Social Networks, pp. 165–192. Springer, Heidelberg (2014)
Beigi, G., Tang, J., Liu, H.: Signed link analysis in social media networks. In: International AAAI Conference on Web and Social Media (ICWSM) (2016)
Davoudi, A., Chatterjee, M.: Modeling trust for rating prediction in recommender systems. In: SIAM Workshop on Machine Learning Methods for Recommender Systems, SIAM, pp. 1–8 (2016)
Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Proceedings of the SDM Workshop on Link Analysis, Counterterrorism and Security (2006)
Wang, X., Sukthankar, G.: Link prediction in multi-relational collaboration networks. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Niagara Falls, Canada, pp. 1445–1447, August 2013
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)
Lü, L., Zhou, T.: Role of weak ties in link prediction of complex networks. In: Proceedings of the ACM International Workshop on Complex networks Meet Information & Knowledge Management, pp. 55–58. ACM (2009)
Murata, T., Moriyasu, S.: Link prediction based on structural properties of online social networks. New Gener. Comput. 26(3), 245–257 (2008)
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 025102 (2001)
Barabási, A.L., et al.: Scale-free networks: a decade and beyond. Science 325(5939), 412 (2009)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co. Inc., Boston (2005)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Snijders, T., van de Bunt, G., Steglich, C.E.G.: Introduction to actor-based models for network dynamics. Soc. Netw. 32, 44–60 (2010)
Acknowledgments
Research at University of Central Florida was supported with an internal Reach for the Stars award. 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.
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Hajibagheri, A., Sukthankar, G., Lakkaraju, K. (2016). Leveraging Network Dynamics for Improved Link Prediction. 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_14
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