Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model

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