Progresses in Link Prediction: A Survey

Abstract

Link prediction is a technique to forecast future new or missing relationships between entities based on the current dynamic network information. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about matrix factorization, probabilistic models, network embedding, deep learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.