Abstract
Often, the information contained in network data is incomplete. Many avenues of research are aimed at addressing this incompleteness. For example, the link prediction problem attempts to identify which missing links are most likely to exist in the complete network. In this paper, we consider a related, but different, problem: predicting the directions of links in a directed network. We treat this problem as a supervised learning problem in which the directions of some edges are known. We calculate various features of each known edge based on its position in the network, and use a Support Vector Machine to predict the unknown directions of edges. We consider four networks, and show that in each case, this method performs significantly better than other compared methods.
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References
Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SIAM Workshop on Link Analysis, Counterterrorism and Security with SIAM Data Mining Conference (2006)
Adar, E., Adamic, L.: Tracking information epidemics in blogspace. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (2005)
Lu, L., Zhou, T.: Link prediction in complex neworks: a survey. Phys. A: Stat. Mech. 390(6), 1150–1170 (2011)
Gup, F., Yang, Z., Zhou, T.: Predicting link directions via a recursive subgraph- based ranking (2012), http://arxiv.org/abs/1206.2199
Freeman, L.C., Borgatti, S.P., White, D.R.: Centrality in valued graphs: a measure of betweenness based on network flow. Social Networks 13, 141–154 (1991)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2 (2011)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the Web. Technical Report, Stanford Digital Libraries (1998)
Stanford Network Analysis Project, http://snap.stanford.edu/index.html (accessed 2012)
Leskovec, J., Adamic, L., Adamic, B.: The dynamics of viral marketing. ACM Transactions on the Web 1(1) (2007)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: CHI 2010, pp. 1361–1370 (2010)
Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6(1), 29–123 (2009)
Richardson, M., Agrawal, R., Domingos, P.: Trust Management for the Semantic Web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)
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Soundarajan, S., Hopcroft, J.E. (2012). Use of Supervised Learning to Predict Directionality of Links in a Network. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_33
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DOI: https://doi.org/10.1007/978-3-642-35527-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35526-4
Online ISBN: 978-3-642-35527-1
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