Link prediction based on path entropy

TL;DR

This paper introduces a novel link prediction method using path entropy from information theory, which considers shortest paths and penalizes longer paths, outperforming existing predictors in real-world networks.

Contribution

The paper proposes the Path Entropy (PE) index, a new similarity measure for link prediction that incorporates path entropy and penalizes longer paths.

Findings

PE index outperforms mainstream link predictors.

Path entropy effectively quantifies path uncertainty.

Method shows strong results on real-world networks.

Abstract

Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream link predictors.