Dataset Information

Mapping temporal-network percolation to weighted, static event graphs.

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.

SUBMITTER: Kivela M

PROVIDER: S-EPMC6098025 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Mapping temporal-network percolation to weighted, static event graphs.

Kivelä Mikko M Cambe Jordan J Saramäki Jari J Karsai Márton M

Scientific reports 20180817 1

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-respecti ...[more]

PMID: 30120278

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Dataset Information

Mapping temporal-network percolation to weighted, static event graphs.

Publications

Mapping temporal-network percolation to weighted, static event graphs.

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