  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.
