Progresses and Challenges in Link Prediction

TL;DR

This paper reviews recent advances and ongoing challenges in link prediction within network science, covering methods like similarity indices, network embedding, and ensemble learning, based on extensive literature from the past decade.

Contribution

It provides a comprehensive summary of progress in link prediction techniques and highlights key challenges for future research in the field.

Findings

Advances in local similarity indices and network embedding.

Improved understanding of link predictability.

Identification of persistent challenges in the field.

Abstract

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.