Centrality measures in networks based on nodes attributes, long-range interactions and group influence

TL;DR

This paper introduces a novel method for evaluating influence in networks by incorporating node attributes, group effects, and interaction strength, enabling detection of both explicit and hidden central nodes beyond traditional measures.

Contribution

The paper presents a new influence assessment method that integrates multiple factors, surpassing classical centrality measures in identifying influential nodes.

Findings

Identifies explicit and hidden central nodes in networks.

Outperforms classical centrality measures in influence detection.

Provides a comprehensive framework for influence analysis.

Abstract

We propose a new method for assessing agents influence in network structures, which takes into consideration nodes attributes, individual and group influences of nodes, and the intensity of interactions. This approach helps us to identify both explicit and hidden central elements which cannot be detected by classical centrality measures or other indices.