Entropy-based link prediction in weighted networks

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.

14 pages, 2 figures

References in corpus (3)

• Predicting Missing Links via Local Information
• Effective and Efficient Similarity Index for Link Prediction of Complex Networks
• Link Prediction in Complex Networks: A Mutual Information Perspective