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Link prediction in complex networks has attracted considerable attention frominterdisciplinary research communities, due to its ubiquitous applications inbiological networks, social networks, transportation networks,telecommunication networks, and, recently, knowledge graphs. Numerous studiesutilized link prediction approaches in order sto find missing links or predictthe likelihood of future links as well as employed for reconstruction networks,recommender systems, privacy control, etc. This work presents an extensivereview of state-of-art methods and algorithms proposed on this subject andcategorizes 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 thispaper, which can be used in order to study link prediction. We conclude thisstudy with a discussion of recent developments and future research directions.

Review on Learning and Extracting Graph Features for Link Prediction