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

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.