Abstract
Identifying influential spreaders in networks, which contributes to optimizing the use of available resources and efficient spreading of information, is of great theoretical significance and practical value. A random-walk-based algorithm LeaderRank has been shown as an effective and efficient method in recognizing leaders in social network, which even outperforms the well-known PageRank method. As LeaderRank is initially developed for binary directed networks, further extensions should be studied in weighted networks. In this paper, a generalized algorithm PhysarumSpreader is proposed by combining LeaderRank with a positive feedback mechanism inspired from an amoeboid organism called Physarum Polycephalum. By taking edge weights into consideration and adding the positive feedback mechanism, PhysarumSpreader is applicable in both directed and undirected networks with weights. By taking two real networks for examples, the effectiveness of the proposed method is demonstrated by comparing with other standard centrality measures.
Highlights
Over the years, the study of graphs and networks have drawn increasing attention in a wide variety of scientific disciplines, such as biology, computer science, economics, mathematics and sociology
We proposed a generalized centrality metric, called PhysarumSpreader, to identify nodes with high spreading performance in networks
It is natural to consider that adoption of the positive feedback mechanism between conductivity and flux in Physarum model may be of great help in overcoming the weakness of LeaderRank in weighted networks
Summary
The study of graphs and networks have drawn increasing attention in a wide variety of scientific disciplines, such as biology, computer science, economics, mathematics and sociology. Newman [32] and Brandes [39] have generalized the closeness and betweenness centrality for weighted networks by using Dijkstra’s algorithm [40] on computing the shortest paths By taking both edge weights and the number of edges into consideration, a new generalization was proposed by Opsahl et al [33], using a tuning parameter to balance the relative importance between those two parts. We proposed a generalized centrality metric, called PhysarumSpreader, to identify nodes with high spreading performance in networks. With simulations on different networks and comparison with other centrality measures, it reveals that PhysarumSpreader works well in identifying influential nodes with high spreading performance and good tolerance.
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