A novel method based on node correlation to evaluate the important nodes in complex networks

TL;DR

This paper introduces a new node importance evaluation method in complex networks that considers both node position and correlation, demonstrating superior performance over existing algorithms.

Contribution

A novel global similarity centrality (GSC) method based on node distribution and correlations, improving importance evaluation accuracy.

Findings

GSC outperforms current state-of-the-art algorithms in experiments.

The method is effective across various artificial and real datasets.

GSC enhances network vulnerability analysis.

Abstract

Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability. Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation, showing that the interaction between nodes has an influence on the importance of nodes. In this paper, a novel method based on node distribution and global influence in complex networks is proposed. Our main idea is that the importance of nodes being linked not only to the relative position in the network but also to the correlations with each other. The nodes in the complex networks are classified according to the distance matrix, then the correlation coefficient between pairs of nodes is calculated. From the whole perspective in the network, the global similarity centrality (GSC) is proposed based on the relevance and shortest distance between any two nodes. The efficiency, accuracy and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes. Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.