Progresses and Challenges in Link Prediction
It provides a comprehensive overview for researchers in network science, but is incremental as it synthesizes existing work rather than introducing new findings.
This perspective paper reviews the problem of link prediction in network science, summarizing key advances from thousands of publications over the past decade, including methods like local similarity indices and network embedding, while outlining ongoing challenges for future research.
Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and metrics of link prediction, this Perspective will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning and others, mainly extracted from thousands of related publications in the last decade. Finally, this Perspective will outline some long-standing challenges for future studies.