Overlapping influence of multiple spreaders in complex networks

With the development of computer technology and the Internet, network science is attracting many scientists from various fields. One field in network science is epidemic spreading, in which the key problem is the selection of source spreaders. Conventional methods select spreaders according to the importance of nodes (degree, betweenness and so on) and nodes with high importance are selected. Traditional methods perform well in characterizing the spreading ability of single nodes, but poorly in multiple nodes. An anahysis is made and the reasons poor performance of multiple spreaders is attributed to the overlapping influences that decrease the overall spreading ability of multiple nodes. Then, an improved method is proposed to suppress the overlapping influences. The validity of the proposed method is illustrated in four real-world networks in which the method could select better multiple spreaders. Further, it was found that improving the sparsity could reduce the overlapping influence of multiple spreaders, which enhances the overall spreading ability of nodes.