Computationally efficient link prediction in a variety of social networks

Michael Fire, Lena Tenenboim-Chekina, Rami Puzis, Ofrit Lesser, Lior Rokach, Yuval Elovici

ACM Transactions on Intelligent Systems and Technology (TIST) 5 (1), 1-25, 2014

Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect acquirement process or because they are not yet reflected in the online network (i.e., friends in the real world did not form a virtual connection). The primary bottleneck in link prediction techniques is extracting the structural features required for classifying links. In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of classifying links between …