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Evaluating metrics in link streams

We seek to understand the topological and temporal nature of temporal networks by computing the distances, latencies and lengths of shortest fastest paths. Shortest fastest paths offer interesting insights about connectivity that were unknowable until recently. Moreover, distances and latencies tend...

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Detalles Bibliográficos
Autor principal: Simard, Frédéric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175937/
https://www.ncbi.nlm.nih.gov/pubmed/34104260
http://dx.doi.org/10.1007/s13278-021-00759-7
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author Simard, Frédéric
author_facet Simard, Frédéric
author_sort Simard, Frédéric
collection PubMed
description We seek to understand the topological and temporal nature of temporal networks by computing the distances, latencies and lengths of shortest fastest paths. Shortest fastest paths offer interesting insights about connectivity that were unknowable until recently. Moreover, distances and latencies tend to be computed by separate algorithms. We developed four algorithms that each compute all those values efficiently as a contribution to the literature. Two of those methods compute metrics from a fixed source temporal node. The other two, as a significant contribution to the literature, compute the metrics between all pairs of source and destination temporal nodes. The methods are also grouped by whether they work on paths with delays or not. Proofs of correctness for our algorithms are presented as well as bounds on their temporal complexities as functions of temporal network parameters. Experimental results show the algorithms presented perform well against the state of the art and terminate in decent time on real-world datasets. One purpose of this study is to help develop algorithms to compute centrality functions on temporal networks such as the betweenness centrality and the closeness centrality.
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spelling pubmed-81759372021-06-04 Evaluating metrics in link streams Simard, Frédéric Soc Netw Anal Min Original Article We seek to understand the topological and temporal nature of temporal networks by computing the distances, latencies and lengths of shortest fastest paths. Shortest fastest paths offer interesting insights about connectivity that were unknowable until recently. Moreover, distances and latencies tend to be computed by separate algorithms. We developed four algorithms that each compute all those values efficiently as a contribution to the literature. Two of those methods compute metrics from a fixed source temporal node. The other two, as a significant contribution to the literature, compute the metrics between all pairs of source and destination temporal nodes. The methods are also grouped by whether they work on paths with delays or not. Proofs of correctness for our algorithms are presented as well as bounds on their temporal complexities as functions of temporal network parameters. Experimental results show the algorithms presented perform well against the state of the art and terminate in decent time on real-world datasets. One purpose of this study is to help develop algorithms to compute centrality functions on temporal networks such as the betweenness centrality and the closeness centrality. Springer Vienna 2021-06-04 2021 /pmc/articles/PMC8175937/ /pubmed/34104260 http://dx.doi.org/10.1007/s13278-021-00759-7 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Simard, Frédéric
Evaluating metrics in link streams
title Evaluating metrics in link streams
title_full Evaluating metrics in link streams
title_fullStr Evaluating metrics in link streams
title_full_unstemmed Evaluating metrics in link streams
title_short Evaluating metrics in link streams
title_sort evaluating metrics in link streams
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175937/
https://www.ncbi.nlm.nih.gov/pubmed/34104260
http://dx.doi.org/10.1007/s13278-021-00759-7
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