Adaptive immunization in dynamic networks

Jiming LIU*, Chao Gao

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 19th International Symposium, ISMIS 2011, Proceedings
Number of pages11
Publication statusPublished - 2011
Event19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011 - Warsaw, Poland
Duration: 28 Jun 201130 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6804 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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