Dynamic communicability predicts infectiousness

Alexander Vassilios Mantzaris, Desmond Higham

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)
136 Downloads (Pure)

Abstract

Using real, time-dependent social interaction data, we look at correlations
between some recently proposed dynamic centrality measures and summaries
from large-scale epidemic simulations. The evolving network arises from email exchanges.
The centrality measures, which are relatively inexpensive to compute, assign
rankings to individual nodes based on their ability to broadcast information over
the dynamic topology. We compare these with node rankings based on infectiousness
that arise when a full stochastic SI simulation is performed over the dynamic
network. More precisely, we look at the proportion of the network that a node is able
to infect over a fixed time period, and the length of time that it takes for a node to infect
half the network.We find that the dynamic centrality measures are an excellent,
and inexpensive, proxy for the full simulation-based measures.
Original languageEnglish
Title of host publicationTemporal networks
Subtitle of host publicationunderstanding complex systems
EditorsPetter Holme, Jari Saramaki
Place of PublicationBerlin
PublisherSpringer
Pages283-294
Number of pages12
ISBN (Print)9783642364600
DOIs
Publication statusPublished - 2013

Keywords

  • dynamic communicability
  • prediction
  • infectiousness
  • social interaction data
  • large-scale epidemic simulations

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  • Cite this

    Mantzaris, A. V., & Higham, D. (2013). Dynamic communicability predicts infectiousness. In P. Holme, & J. Saramaki (Eds.), Temporal networks: understanding complex systems (pp. 283-294). Springer. https://doi.org/10.1007/978-3-642-36461-7_14