A voting approach to uncover multiple influential spreaders on weighted networks

The identification of multiple spreaders on weighted complex networks is a crucial step towards efficient information diffusion, preventing epidemics spreading and etc. In this paper, we propose a novel approach WVoteRank to find multiple spreaders by extending VoteRank. VoteRank has limitations to select multiple spreaders on unweighted networks while various real networks are weighted networks such as trade networks, traffic flow networks and etc. Thus our approach WVoteRank is generalized to deal with both unweighted and weighted networks by considering both degree and weight in voting process. Experimental studies on LFR synthetic networks and real networks show that in the context of Susceptible–Infected–Recovered (SIR) propagation, WVoteRank outperforms existing states of arts methods such as weighted H-index, weighted K-shell, weighted degree centrality and weighted betweeness centrality on final affected scale. It obtains an improvement of final affected scale as much as 8.96%. Linear time complexity enables it to be applied on large networks effectively.