Entropy-based link prediction in weighted networks

• Published in: 中国物理B：英文版 no. 1; pp. 588 - 594
• Main Author: 许忠奇 濮存来 Rajput Ramiz Sharafat 李伦波 杨健
• Format: Journal Article
• Language: English
• Published: 2017
• ISSN: 1674-1056; 2058-3834

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks.In a previous work [Xu et al. Physica A, 456 294(2016)], 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 other typical weighted indices.