title: An efficient immunization strategy for community networks.
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.
authors.affiliation: 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.
authors.forename: Kai
authors.initials: K
authors.lastname: Gong
authors.affiliation: 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.
authors.forename: Ming
authors.initials: M
authors.lastname: Tang
authors.affiliation: Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong, People's Republic of China.
authors.forename: Pak Ming
authors.initials: PM
authors.lastname: Hui
authors.affiliation: School of Mathematical Science, Anhui University, Hefei, People's Republic of China.
authors.forename: Hai Feng
authors.initials: HF
authors.lastname: Zhang
authors.affiliation: Department of Mathematics, Kyungpook National University, Daegu, South Korea.
authors.forename: Do
authors.initials: D
authors.lastname: Younghae
authors.affiliation: 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.
authors.forename: Ying-Cheng
authors.initials: YC
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D006801:Humans; D007114:Immunization / Q000379:methods* / Q000706:statistics & numerical data; D060756:Social Networking; D013223:Statistics as Topic / Q000379:methods*; D014495:Universities