Entropy-based link prediction in weighted networks

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 con...

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Bibliographic Details
Published inChinese physics B Vol. 26; no. 1; pp. 584 - 590
Main Author 许忠奇 濮存来 Rajput Ramiz Sharafat 李伦波 杨健
Format Journal Article
LanguageEnglish
Published 2017
Subjects
信息熵
加权指数
加权网络
复杂网络
路径
链路
预测指标
预测精度
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ISSN1674-1056
2058-3834
DOI10.1088/1674-1056/26/1/018902

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Summary: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.
Bibliography:link prediction; weighted networks; information entropy
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.
Zhongqi Xu1,Cunlai Pu1,2,Rajput Ramiz Sharafat1,Lunbo Li1,Jian Yang1（1. Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; 2.Department of Industrial and Systems Engineering, University of Florida, Gainesville 32611, USA）
11-5639/O4
ISSN:1674-1056
2058-3834
DOI:10.1088/1674-1056/26/1/018902