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PhysarumSpreader: a new bio-Inspired methodology for identifying influential spreaders in complex networks

Wang, Hongping, Zhang, Yajuan, Zhang, Zili, Mahadevan, Sankaran and Deng, Yong 2015, PhysarumSpreader: a new bio-Inspired methodology for identifying influential spreaders in complex networks, PLoS, vol. 10, no. 12, pp. 1-21, doi: 10.1371/journal.pone.0145028.

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Title PhysarumSpreader: a new bio-Inspired methodology for identifying influential spreaders in complex networks
Author(s) Wang, Hongping
Zhang, Yajuan
Zhang, Zili
Mahadevan, Sankaran
Deng, Yong
Journal name PLoS
Volume number 10
Issue number 12
Start page 1
End page 21
Total pages 21
Publisher PLoS
Place of publication San Francisco, Calif.
Publication date 2015-12-18
ISSN 1932-6203
Keyword(s) Algorithms
Computer Simulation
Feedback
Models, Biological
Physarum polycephalum
Summary 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.
Language eng
DOI 10.1371/journal.pone.0145028
Field of Research 080503 Networking and Communications
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, PLoS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081347

Document type: Journal Article
Collection: School of Information Technology
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