Entropy-based link prediction in weighted networks

TL;DR

This paper introduces Weighted Path Entropy (WPE), a new link prediction method for weighted networks that combines path entropy and path weight to improve accuracy over existing indices.

Contribution

It proposes a novel weighted prediction index, WPE, that enhances link prediction by integrating path entropy and weights, outperforming traditional methods.

Findings

WPE achieves higher prediction accuracy than three typical weighted indices.

Empirical results on six real-world networks validate the effectiveness of WPE.

Abstract

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 \cite{xu2016}), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.