Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks

Xunyi Zhao, Huijuan Wang

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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.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications IX
Subtitle of host publicationProceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
EditorsRosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
Place of PublicationCham
Number of pages11
ISBN (Electronic)978-3-030-65347-7
ISBN (Print)978-3-030-65346-0
Publication statusPublished - 2021
Event9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020 - Madrid, Spain
Duration: 1 Dec 20203 Dec 2020

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


Conference9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020

Bibliographical note

Accepted author manuscript


  • Epidemic mitigation
  • SI spreading
  • Temporal network

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