Link Prediction in Complex Networks: A Mutual Information Perspective

TL;DR

This paper introduces a mutual information-based approach to link prediction in complex networks, leveraging information theory to enhance accuracy while maintaining manageable computational complexity.

Contribution

It reexamines network topology's role in link prediction through mutual information, offering a practical method that outperforms traditional topological measures.

Findings

Significant improvement in link prediction accuracy.

Maintains reasonable computational complexity.

Provides a novel information-theoretic perspective.

Abstract

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.