Maximizing the likelihood of detecting outbreaks in temporal networks
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Author (Corporation)
Publication date
2020
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Collections
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04B - Conference paper
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Parent work
Complex networks and their applications VIII. Volume 2 proceedings of the eighth international conference on complex networks and their applications. Complex Networks 2019
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DOI of the original publication
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Series
Studies in Computational Intelligence
Series number
882
Volume
Issue / Number
Pages / Duration
481-493
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Publisher / Publishing institution
Springer
Place of publication / Event location
Cham
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Abstract
Epidemic spreading occurs among animals, humans, or computers and causes substantial societal, personal, or economic losses if left undetected. Based on known temporal contact networks, we propose an outbreak detection method that identifies a small set of nodes such that the likelihood of detecting recent outbreaks is maximal. The two-step procedure involves (i) simulating spreading scenarios from all possible seed configurations and (ii) greedily selecting nodes for monitoring in order to maximize the detection likelihood. We find that the detection likelihood is a submodular set function for which it has been proven that greedy optimization attains at least 63% of the optimal (intractable) solution. The results show that the proposed method detects more outbreaks than benchmark methods suggested recently and is robust against badly chosen parameters. In addition, our method can be used for outbreak source detection. A limitation of this method is its heavy use of computational resources. However, for large graphs the method could be easily parallelized.
Keywords
Subject (DDC)
330 - Wirtschaft
Event
8th International Conference on Complex Networks and their Applications
Exhibition start date
Exhibition end date
Conference start date
10.12.2019
Conference end date
12.12.2019
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ISBN
978-3-030-36682-7
978-3-030-36683-4
978-3-030-36683-4
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
Open access category
Closed
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Citation
STERCHI, Martin, Cristina SARASUA, Rolf GRÜTTER und Abraham BERNSTEIN, 2020. Maximizing the likelihood of detecting outbreaks in temporal networks. In: Hocine CHERIFI, Sabrina GAITO, José Fernendo MENDES, Esteban MORO und Luis Mateus ROCHA (Hrsg.), Complex networks and their applications VIII. Volume 2 proceedings of the eighth international conference on complex networks and their applications. Complex Networks 2019. Cham: Springer. 2020. S. 481–493. Studies in Computational Intelligence, 882. ISBN 978-3-030-36682-7. Verfügbar unter: https://irf.fhnw.ch/handle/11654/42604