Link prediction techniques, applications, and performance: A survey.

Link prediction finds missing links (in static networks) or predicts the likelihood of future links (in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; Barabasi and Albert, 1999; Kleinberg, 2000; Leskovec et al., 2005; Zhang et al., 2015). Link prediction is a fast-growing research area in both physics and computer science domain. There exists a wide range of link prediction techniques like similarity-based indices, probabilistic methods, dimensionality reduction approaches, etc., which are extensively explored in different groups of this article. Learning-based methods are covered in addition to clustering-based and information-theoretic models in a separate group. The experimental results of similarity and some other representative approaches are tabulated and discussed. To make it general, this review also covers link prediction in different types of networks, for example, directed, temporal, bipartite, and heterogeneous networks. Finally, we discuss several applications with some recent developments and concludes our work with some future works. • We ponder over the problem of link prediction in social networks. • We reviewed several approaches to link prediction from classical to recent network embedding and deep learning approaches. • We put forward taxonomy of the existing approaches that clearly groups these approaches into well defined domains. • We experimentally evaluated the structural-based approaches using four evaluation metrics. • Finally, we explored different applications, recent developments, and future directions. [ABSTRACT FROM AUTHOR]