摘要
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.
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.
作者
许忠奇
濮存来
Rajput Ramiz Sharafat
李伦波
杨健
Zhongqi Xu Cunlai Pu Rajput Ramiz Sharafat Lunbo Li Jian Yang(Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China Department of Industrial and Systems Engineering, University of Florida, Gainesville 32611, USA)
基金
supported by the National Natural Science Foundation of China(Grant Nos.61201173 and 61304154)
the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20133219120032)
the Postdoctoral Science Foundation of China(Grant No.2013M541673)
China Postdoctoral Science Special Foundation(Grant No.2015T80556)
