On the guarantee of containment probability in influence minimization

Chien Wei Chang, Mi Yen Yeh, Kun Ta Chuang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

We in this paper explore a novel model of influence minimization for the need to effectively prevent the outbreak of epidemic-prone spread on networks. The current network-blocking models usually report the expected number of infected nodes under the limited number of cutting edges. However, to control the epidemic-prone spread such as dengue fever, epidemiologists tend to deploy a cost-effective intervention with low outbreak risk, but the outbreak risk cannot be estimated based on the expectation of infected count. We in this paper explore the first solution to estimate the probability that can successfully bound the infected count below the out-of-control threshold, which can be logically mapped to the outbreak risk and can facilitate the authority to adaptively adjust the intervention cost for the need of risk control. We elaborate upon the proposed MCP (standing for Maximization of Containment Probability) problem and show that it is a NP-hard challenge without the submodular property. We further devise an effective measurement of sufficient number of Monte Carlo iterations based on the relative error of Monte Carol integration. The experimental results show that our proposed algorithm with small iterations can deliver the qualified guarantee of containment probability, demonstrating its feasibility for real applications.

原文English
主出版物標題Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
編輯Ravi Kumar, James Caverlee, Hanghang Tong
發行者Institute of Electrical and Electronics Engineers Inc.
頁面231-238
頁數8
ISBN(電子)9781509028467
DOIs
出版狀態Published - 2016 十一月 21
事件2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
持續時間: 2016 八月 182016 八月 21

出版系列

名字Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
國家United States
城市San Francisco
期間16-08-1816-08-21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

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