LAUSR

The evolutionary behavior of temporal networks has gained the attention of researchers with its ubiquitous applications in a variety of real-world scenarios. Learning evolutionary behavior of networks is directly related to link prediction problem, as the addition or removal of new links or edges over time leads to the network evolution. With the rise of large-scale temporal networks such as social networks, temporal link prediction has become an interesting field of study. In this work, we provide a detailed survey of various researches carried out in the direction of temporal link prediction. We build a taxonomy of temporal link prediction methods based on various approaches used and discuss the works which come under each category. Further, we present the challenges and directions for future works.