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Relevance of temporal cores for epidemic spread in temporal networks

Authors
  • Ciaperoni, Martino1
  • Galimberti, Edoardo2
  • Bonchi, Francesco2
  • Cattuto, Ciro2, 3
  • Gullo, Francesco4
  • Barrat, Alain2, 5, 6
  • 1 Aalto University, Espoo, Finland , Espoo (Finland)
  • 2 ISI Foundation, Turin, 10126, Italy , Turin (Italy)
  • 3 University of Turin, Turin, Italy , Turin (Italy)
  • 4 UniCredit, Milan, Italy , Milan (Italy)
  • 5 Aix Marseille Univ, Université de Toulon, Marseille, 13288, France , Marseille (France)
  • 6 Tokyo Institute of Technology, Tokyo, Japan , Tokyo (Japan)
Type
Published Article
Journal
Scientific Reports
Publisher
Springer Nature
Publication Date
Jul 27, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41598-020-69464-3
Source
Springer Nature
License
Green

Abstract

Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The identification of structures playing important roles in such processes remains largely an open question, despite recent progresses in the case of static networks. Here, we consider as candidate structures the recently introduced concept of span-cores: the span-cores decompose a temporal network into subgraphs of controlled duration and increasing connectivity, generalizing the core-decomposition of static graphs. To assess the relevance of such structures, we explore the effectiveness of strategies aimed either at containing or maximizing the impact of a spread, based respectively on removing span-cores of high cohesiveness or duration to decrease the epidemic risk, or on seeding the process from such structures. The effectiveness of such strategies is assessed in a variety of empirical data sets and compared to baselines that use only static information on the centrality of nodes and static concepts of coreness, as well as to a baseline based on a temporal centrality measure. Our results show that the most stable and cohesive temporal cores play indeed an important role in epidemic processes on temporal networks, and that their nodes are likely to include influential spreaders.

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