# Identifying influential nodes based on fuzzy local dimension in complex networks

## TL;DR

This paper introduces a fuzzy local dimension method to identify influential nodes in complex networks, demonstrating its effectiveness through real-world data and spreading simulations.

## Contribution

A novel fuzzy local dimension approach is proposed, incorporating fuzzy set theory to improve the accuracy of influential node identification in complex networks.

## Key findings

- The method effectively ranks influential nodes in real-world networks.
- Fuzzy local dimension correlates well with spreading influence in simulations.
- The approach outperforms traditional centrality measures in accuracy.

## Abstract

How to identify influential nodes in complex networks is an important aspect in the study of complex network. In this paper, a novel fuzzy local dimension (FLD) is proposed to rank the influential nodes in complex networks, where a node with high fuzzy local dimension has high influential ability. This proposed method focuses on the influence of the distance from the center node on the local dimension of center node by fuzzy set, resulting in a change in influential ability. In order to show this proposed method's effectiveness and accuracy, four real-world networks are applied in this paper. Meanwhile, Susceptible-Infected (SI) is used to simulate the spreading process by FLD and other centrality measures, and Kendall's tau coefficient is used to describe the correlation between the influential nodes obtained by centrality and the results measured by SI model. Observing from the ranking lists and simulated results, this method is effective and accurate to rank the influential nodes.