Overlapping influence inspires the selection of multiple spreaders in complex networks

Accelerating or controlling the spread of epidemics in complex networks has received considerable attention in recent years. The key issue is to choose appropriate set of initial spreaders (or immunization nodes). Most previous work selects spreaders depending on the centralities of nodes, ignoring the coupling effects between nodes. In this paper, by considering the overlapping influences (coupling effects), a novel framework is proposed to upgrade the collective influence of multiple spreaders. The proposed framework could select influential spreaders, yet with low overlapping influences. Comparing with state of the art methods, the collective influence of the spreaders by our method is improved. Based on SIR model, experimental results in real networks illustrate the effectiveness of our method.