Link Prediction in Complex Networks: A Survey

TL;DR

This survey reviews recent advances in link prediction algorithms for complex networks, focusing on physical approaches like random walks and maximum likelihood, and discusses applications and future challenges.

Contribution

It provides a comprehensive overview of physical perspective-based link prediction methods and their applications, highlighting recent progress and future directions.

Findings

Summarizes recent progress in link prediction algorithms.

Highlights physical approaches like random walks and maximum likelihood.

Discusses applications such as network reconstruction and evolution evaluation.

Abstract

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.