A New Ranking Technique to Enhance the Infection Size in Complex Networks

Abstract

Detecting the spreaders/sources in complex networks is an essential manner to understand the dynamics of the information spreading process. Consider the k-Shell centrality metric, which is taken into account the structural position of a node within the network, a more effective metric in picking the node which has more ability on spreading the infection compared to other centrality metrics such the degree, between and closeness.  However, the K-Shell method suffers from some boundaries, it gives the same K-Shell index to a lot of the nodes, and it uses only one indicator to rank the nodes. A new technique is proposed in this research to develop the K-Shell metric by using the degree of the node, and a coreness of its rounding friends to estimate the ability of the node in spreading the infection within the network. The experimental results, which were done on four types of real and synthetic networks, and using an epidemic propagation model SIR, demonstrate that the suggested technique can measure the node effect more precisely and offer a unique ordering group than other centrality measures.