title: PhysarumSpreader: A New Bio-Inspired Methodology for Identifying Influential Spreaders in Complex Networks.
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.
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing, China.
authors.forename: Hongping
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing, China.
authors.forename: Yajuan
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing, China.|School of Information Technology, Deakin University, Geelong, VIC, Australia.
authors.forename: Zili
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Civil and Environmental Engineering Department, Vanderbilt University, Nashville, TN, United States of America.
authors.forename: Sankaran
authors.initials: S
authors.lastname: Mahadevan
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008954:Models, Biological; D016839:Physarum polycephalum / Q000502:physiology*