Mapping temporal-network percolation to weighted, static event graphs

Mikko Kivelä 1 Jordan Cambe 1 Jari Saramäki 1 Màrton Karsai 2
2 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : The dynamics of diffusion-like processes on temporal networks are influenced by correlations in the times of contacts. This influence is particularly strong for processes where the spreading agent has a limited lifetime at nodes: disease spreading (recovery time), diffusion of rumors (lifetime of information), and passenger routing (maximum acceptable time between transfers). We introduce weighted event graphs as a powerful and fast framework for studying connectivity determined by time-respecting paths where the allowed waiting times between contacts have an upper limit. We study percolation on the weighted event graphs and in the underlying temporal networks, with simulated and real-world networks. We show that this type of temporal-network percolation is analogous to directed percolation, and that it can be characterized by multiple order parameters.
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https://hal.inria.fr/hal-01952142
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Submitted on : Tuesday, December 11, 2018 - 9:02:46 PM
Last modification on : Thursday, February 7, 2019 - 3:37:48 PM

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Mikko Kivelä, Jordan Cambe, Jari Saramäki, Màrton Karsai. Mapping temporal-network percolation to weighted, static event graphs. Scientific Reports, Nature Publishing Group, 2018, 8 (1), ⟨10.1038/s41598-018-29577-2⟩. ⟨hal-01952142⟩

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