A comprehensive survey of link prediction methods

Link prediction aims to anticipate the probability of a future connection between two nodes in a given network based on their previous interactions and the network structure. Link prediction is a rapidly evolving field of research that has attracted interest from physicists and computer scientists. Over the years, numerous methods have been developed for link prediction, encompassing similarity-based indices, machine learning techniques, and more. While existing surveys have covered link prediction research until 2020, there has been a substantial surge in research activities in recent years, particularly between 2021 and 2023. This increased interest underscores the pressing need to comprehensively explore the latest advancements and approaches in link prediction. We analyse and present the most notable research from 2018 to 2023. Our goal is to offer a comprehensive overview of the recent developments in the field. Besides summarizing and presenting previous experimental results, our survey offers a comprehensive analysis highlighting the strengths and limitations of various link prediction methods.