Measure the similarity of nodes in the complex networks

TL;DR

This paper introduces a new method combining degree centrality and relative entropy to measure node similarity in complex networks, revealing structural and influence-related patterns among nodes.

Contribution

A novel similarity measure based on degree centrality and relative entropy for analyzing nodes in complex networks.

Findings

Nodes with similar structural properties have high similarity.

Highly influential nodes tend to have low similarity to others.

The proposed method effectively captures node similarities.

Abstract

Measure the similarity of the nodes in the complex networks have interested many researchers to explore it. In this paper, a new method which is based on the degree centrality and the Relative-entropy is proposed to measure the similarity of the nodes in the complex networks. The results in this paper show that, the nodes which have a common structure property always have a high similarity to others nodes. The nodes which have a high influential to others always have a small value of similarity to other nodes and the marginal nodes also have a low similar to other nodes. The results in this paper show that the proposed method is useful and reasonable to measure the similarity of the nodes in the complex networks.