Identifying multiple influential spreaders with local relative weakening effect in complex networks

Identifying influential nodes to maximize the spreading influence is an important issue for the study of complex networks. In this paper, we propose a Local Relative Weakening Effect (LRWE) method to identify multiple influential spreaders. Both the weakening effect of selected spreaders and local relative strength are taken into account at the same time. Additionally, the LRWE method can provide a good tradeoff between spreading probability and network topology. The Susceptible-Infected-Recovered (SIR) model is applied for four empirical networks and six null models to evaluate the performance of the LRWE method. Results show that the LRWE method can make the final spreading size largest at the fastest speed. Besides, it can also make the selected spreaders dispersedly distributed and avoid overlapping. Moreover, the LRWE method can adjust the selection of multiple influential spreaders according to the spreading probability too. Copyright (C) EPLA, 2018