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PLoS One. 2013 Dec 20;8(12):e83489. doi: 10.1371/journal.pone.0083489. eCollection 2013.

An efficient immunization strategy for community networks.

Author information

1
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China ; Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong, People's Republic of China.
2
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China ; Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong, People's Republic of China ; Department of Mathematics, Kyungpook National University, Daegu, South Korea.
3
Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong, People's Republic of China.
4
School of Mathematical Science, Anhui University, Hefei, People's Republic of China.
5
Department of Mathematics, Kyungpook National University, Daegu, South Korea.
6
Department of Mathematics, Kyungpook National University, Daegu, South Korea ; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, United States of Ameica.

Abstract

An efficient algorithm that can properly identify the targets to immunize or quarantine for preventing an epidemic in a population without knowing the global structural information is of obvious importance. Typically, a population is characterized by its community structure and the heterogeneity in the weak ties among nodes bridging over communities. We propose and study an effective algorithm that searches for bridge hubs, which are bridge nodes with a larger number of weak ties, as immunizing targets based on the idea of referencing to an expanding friendship circle as a self-avoiding walk proceeds. Applying the algorithm to simulated networks and empirical networks constructed from social network data of five US universities, we show that the algorithm is more effective than other existing local algorithms for a given immunization coverage, with a reduced final epidemic ratio, lower peak prevalence and fewer nodes that need to be visited before identifying the target nodes. The effectiveness stems from the breaking up of community networks by successful searches on target nodes with more weak ties. The effectiveness remains robust even when errors exist in the structure of the networks.

PMID:
24376708
PMCID:
PMC3869806
DOI:
10.1371/journal.pone.0083489
[Indexed for MEDLINE]
Free PMC Article
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