Print Email Facebook Twitter Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks Title Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks Author Zhao, Xunyi (Student TU Delft) Wang, H. (TU Delft Multimedia Computing) Contributor Benito, Rosa M. (editor) Cherifi, Chantal (editor) Cherifi, Hocine (editor) Moro, Esteban (editor) Rocha, Luis Mateus (editor) Sales-Pardo, Marta (editor) Date 2021 Abstract In this paper, we aim to effectively suppress the spread of epidemic/information via blocking/removing a given fraction of the contacts in a temporal (time evolving) human contact network. We consider the SI (Susceptible- Infected) spreading process, on a temporal contact network to illustrate our methodology: an infected node infects a susceptible node with a probability β when a contact happens between the two nodes. We address the question: which contacts should be blocked in order to minimize the average prevalence over time. We firstly propose systematically a set of link properties (centrality metrics) based on the aggregated network of a temporal network, that captures the number of contacts between each node pair. Furthermore, we define the probability that a contact c(i, j, t) is removed as a function of the centrality of the corresponding link l(i, j) in the aggregated network as well as the time t of the contact. Each of the centrality metrics proposed can be thus regarded as a contact removal strategy. Empirical results on six temporal contact networks show that the epidemic can be better suppressed if contacts between node pairs that have fewer contacts are more likely to be removed and if contacts happened earlier are likely removed. A strategy tends to perform better when the average number contacts removed per node pair has a lower variance. Strategies that lead to a lower largest eigenvalue of the aggregated network after contact removal do not mitigate the spreading better. This contradicts the finding in static networks, that a network with a small largest eigenvalue tends to be robust against epidemic spreading, illustrating the complexity introduced by the underlying temporal networks. Subject Epidemic mitigationSI spreadingTemporal network To reference this document use: http://resolver.tudelft.nl/uuid:bc630552-8061-47d7-b87b-ded59fcf3834 DOI https://doi.org/10.1007/978-3-030-65347-7_37 Publisher Springer, Cham Embargo date 2021-12-20 ISBN 978-3-030-65346-0 Source Complex Networks and Their Applications IX: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020, 1 Event 9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020, 2020-12-01 → 2020-12-03, Madrid, Spain Series Studies in Computational Intelligence, 1860-949X, 943 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type conference paper Rights © 2021 Xunyi Zhao, H. Wang Files PDF Suppressing_Epidemic_Spre ... orks_2.pdf 552.06 KB Close viewer /islandora/object/uuid:bc630552-8061-47d7-b87b-ded59fcf3834/datastream/OBJ/view