PhysarumSpreader: A New Bio-Inspired Methodology for Identifying Influential Spreaders in Complex Networks

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...

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Bibliographic Details
Published in:PloS one 2015-12, Vol.10 (12), p.e0145028-e0145028
Main Authors: Wang, Hongping, Zhang, Yajuan, Zhang, Zili, Mahadevan, Sankaran, Deng, Yong
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Biology
Collaboration
Computer Simulation
Connectionism
Feedback
Information dissemination
Information science
Mathematical models
Models, Biological
Mold
Networks
Physarum
Physarum polycephalum
Physarum polycephalum - physiology
Physics
Positive feedback
Science
Search engines
Social networks
Social organization
Sociology
Spreaders
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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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0145028