## 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.

Original language | English |
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Title of host publication | Complex Networks and Their Applications IX |

Subtitle of host publication | Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020 |

Editors | Rosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo |

Place of Publication | Cham |

Publisher | Springer |

Pages | 444-454 |

Number of pages | 11 |

Volume | 1 |

ISBN (Electronic) | 978-3-030-65347-7 |

ISBN (Print) | 978-3-030-65346-0 |

DOIs | |

Publication status | Published - 2021 |

Event | 9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020 - Madrid, Spain Duration: 1 Dec 2020 → 3 Dec 2020 |

### Publication series

Name | Studies in Computational Intelligence |
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Volume | 943 |

ISSN (Print) | 1860-949X |

ISSN (Electronic) | 1860-9503 |

### Conference

Conference | 9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020 |
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Country | Spain |

City | Madrid |

Period | 1/12/20 → 3/12/20 |

### Bibliographical note

Accepted author manuscript## Keywords

- Epidemic mitigation
- SI spreading
- Temporal network