Evaluating importance of nodes in complex networks with local volume information dimension

TL;DR

This paper introduces a novel method called local volume information dimension for evaluating node importance in complex networks, considering multi-distance node information with entropy, and demonstrates its effectiveness through experiments on real-world networks.

Contribution

The paper proposes a new local volume information dimension approach that incorporates multi-distance node information using entropy, improving importance evaluation in complex networks.

Findings

The method effectively identifies important nodes in real-world networks.

Experimental results outperform existing importance measures.

The approach provides a comprehensive view of node significance.

Abstract

How to evaluate the importance of nodes is essential in research of complex network. There are many methods proposed for solving this problem, but they still have room to be improved. In this paper, a new approach called local volume information dimension is proposed. In this method, the sum of degree of nodes within different distances of central node is calculated. The information within the certain distance is described by the information entropy. Compared to other methods, the proposed method considers the information of the nodes from different distances more comprehensively. For the purpose of showing the effectiveness of the proposed method, experiments on real-world networks are implemented. Promising results indicate the effectiveness of the proposed method.