  Link prediction in complex networks has attracted considerable attention from
interdisciplinary research communities, due to its ubiquitous applications in
biological networks, social networks, transportation networks,
telecommunication networks, and, recently, knowledge graphs. Numerous studies
utilized link prediction approaches in order sto find missing links or predict
the likelihood of future links as well as employed for reconstruction networks,
recommender systems, privacy control, etc. This work presents an extensive
review of state-of-art methods and algorithms proposed on this subject and
categorizes them into four main categories: similarity-based methods,
probabilistic methods, relational models, and learning-based methods.
Additionally, a collection of network data sets has been presented in this
paper, which can be used in order to study link prediction. We conclude this
study with a discussion of recent developments and future research directions.
