TY - GEN
T1 - Adaptive immunization in dynamic networks
AU - LIU, Jiming
AU - Gao, Chao
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - In recent years, immunization strategies have been developed for stopping epidemics in complex-network-like environments. So far, there exist two limitations in the current propagation models and immunization strategies: (1) the propagation models focus only on the network structure underlying virus propagation and the models are static; (2) the immunization strategies are offline and non-adaptive in nature, i.e., these strategies pre-select and pre-immunize "important" nodes before virus propagation starts. In this paper, we extend an interactive email propagation model in order to observe the effects of human behaviors on virus propagation, and furthermore we propose an adaptive AOC-based immunization strategy for protecting dynamically-evolving email networks. Our experimental results have shown that our strategy as an online strategy can adapt to the dynamic changes (e.g., growth) of networks.
AB - In recent years, immunization strategies have been developed for stopping epidemics in complex-network-like environments. So far, there exist two limitations in the current propagation models and immunization strategies: (1) the propagation models focus only on the network structure underlying virus propagation and the models are static; (2) the immunization strategies are offline and non-adaptive in nature, i.e., these strategies pre-select and pre-immunize "important" nodes before virus propagation starts. In this paper, we extend an interactive email propagation model in order to observe the effects of human behaviors on virus propagation, and furthermore we propose an adaptive AOC-based immunization strategy for protecting dynamically-evolving email networks. Our experimental results have shown that our strategy as an online strategy can adapt to the dynamic changes (e.g., growth) of networks.
UR - http://www.scopus.com/inward/record.url?scp=79960116105&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21916-0_71
DO - 10.1007/978-3-642-21916-0_71
M3 - Conference proceeding
AN - SCOPUS:79960116105
SN - 9783642219153
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 673
EP - 683
BT - Foundations of Intelligent Systems - 19th International Symposium, ISMIS 2011, Proceedings
T2 - 19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011
Y2 - 28 June 2011 through 30 June 2011
ER -