Lexical Sorting Centrality to Distinguish Spreading Abilities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered (SIR) Model

TL;DR

This paper introduces Lexical Sorting Centrality (LSC), a new measure that effectively identifies the spreading ability of nodes in complex networks under the SIR epidemic model, outperforming traditional centrality measures.

Contribution

The paper proposes a novel centrality measure called LSC that improves the accuracy and speed of identifying influential nodes in epidemic spreading within complex networks.

Findings

LSC outperforms traditional centralities in identifying spreading nodes.

LSC is faster and more decisive in simulations.

Experimental results on six datasets validate LSC's effectiveness.

Abstract

Epidemic modeling in complex networks has become one of the latest topics in recent times. The Susceptible-Infectious-Recovered (SIR) model and its variants are often used for epidemic modeling. One important issue in epidemic modeling is the determination of the spreading ability of the nodes in the network. Thus, for example, can be detected in the early stages. In this study, we developed a centrality measure called Lexical Sorting Centrality (LSC) that distinguishes the spreading ability of the nodes. Using other centrality measures calculated for the nodes, LSC sorts the nodes in a way similar to alphabetical order. We conducted simulations on six datasets using SIR to evaluate the performance of LSC and compared LSC with degree centrality (DC), eigenvector centrality (EC), closeness centrality (CC), betweenness centrality (BC) and Gravitational Centrality (GC). Experimental results show that LSC distinguishes the spreading ability of the nodes more accurately, more decisively, and faster.