Node Similarity Measure for Complex Networks

• Published in: Jisuanji kexue yu tansuo
• Main Author: MU Junfang, LIANG Jiye, ZHENG Wenping, LIU Shaoqian, WANG Jie
• Format: Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2020-05-01
• Subjects: complex network; node similarity; node distance distribution; relative entropy
• Online Access: http://fcst.ceaj.org/CN/abstract/abstract2188.shtml

Quantifying similarity between nodes is a fundamental and challenging task in many fields of complex network. The similarity measure based on neighborhood nodes only considers the information of neighbors. The similarity measure based on path considers the information of path, which makes large nodes become general node. In order to more accurately measure the similarity between nodes and avoid the majority of nodes being similar to large nodes, this paper defines the distance distribution of each node, and based on this, it proposes a node similarity measurement method based on distance distribution and relative entropy (DDRE). The DDRE method generates the distance distribution of each node through the shortest path between nodes. According to the distance distribution, the relative entropy between nodes is calculated and the similarity between nodes is obtained. The experimental results of 6 real network data sets show that the DDRE method performs well in both the symmetry and the ability to affect other nodes in the SIR model.