Identification of influencers in complex networks by local information dimensionality

TL;DR

This paper introduces a new method for identifying influential nodes in complex networks based on local information dimensionality, which considers local structural properties and reduces computational complexity, outperforming existing measures.

Contribution

The paper proposes a novel local information dimensionality measure that effectively identifies influential nodes with lower computational costs compared to traditional centrality measures.

Findings

The proposed method outperforms five existing centrality measures in six real-world networks.

Higher local information dimensionality correlates with greater node influence.

The method demonstrates computational efficiency and effectiveness in influence ranking.

Abstract

The identification of influential spreaders in complex networks is a popular topic in studies of network characteristics. Many centrality measures have been proposed to address this problem, but most have limitations. In this paper, a method for identifying influencers in complex networks via the local information dimensionality. The proposed method considers the local structural properties around the central node; therefore, the scale of locality only increases to half of the maximum value of the shortest distance from the central node. Thus, the proposed method considers the quasilocal information and reduces the computational complexity. The information (number of nodes) in boxes is described via the Shannon entropy, which is more reasonable. A node is more influential when its local information dimensionality is higher. In order to show the effectiveness of the proposed method, five existing centrality measures are used as comparison methods to rank influential nodes in six real world complex networks. In addition, a susceptible infected (SI) model and Kendall's tau coefficient are applied to show the correlation between different methods. Experiment results show the superiority of the proposed method.