• 网络与信息安全 •

### 面向复杂网络的节点相似性度量

1. 1. 山西大学 计算机与信息技术学院，太原 030006
2. 山西大学 计算智能与中文信息处理教育部重点实验室，太原 030006
• 出版日期:2020-05-01 发布日期:2020-05-08

### Node Similarity Measure for Complex Networks

MU Junfang, LIANG Jiye, ZHENG Wenping, LIU Shaoqian, WANG Jie

1. 1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
• Online:2020-05-01 Published:2020-05-08

Abstract:

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.