Cargando…

Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks

To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe h...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Mingwu, Rao, Vikyath D., Gernat, Tim, Dankowicz, Harry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768694/
https://www.ncbi.nlm.nih.gov/pubmed/29335422
http://dx.doi.org/10.1038/s41598-017-18450-3
_version_ 1783292751199076352
author Li, Mingwu
Rao, Vikyath D.
Gernat, Tim
Dankowicz, Harry
author_facet Li, Mingwu
Rao, Vikyath D.
Gernat, Tim
Dankowicz, Harry
author_sort Li, Mingwu
collection PubMed
description To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe how temporal features affect spreading dynamics on temporal networks. However, PT implicitly assumes that edges and nodes are continuously active during the network sampling period – an assumption that does not always hold in real networks. We systematically analyze a recently-proposed restriction of PT that preserves node lifetimes (PTN), and a similar restriction (PTE) that also preserves edge lifetimes. We use PT, PTN, and PTE to characterize spreading dynamics on (i) synthetic networks with heterogeneous edge lifespans and tunable burstiness, and (ii) four real-world networks, including two in which nodes enter and leave the network dynamically. We find that predictions of spreading speed can change considerably with the choice of reference model. Moreover, the degree of disparity in the predictions reflects the extent of node/edge turnover, highlighting the importance of using lifetime-preserving reference models when nodes or edges are not continuously present in the network.
format Online
Article
Text
id pubmed-5768694
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-57686942018-01-25 Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks Li, Mingwu Rao, Vikyath D. Gernat, Tim Dankowicz, Harry Sci Rep Article To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe how temporal features affect spreading dynamics on temporal networks. However, PT implicitly assumes that edges and nodes are continuously active during the network sampling period – an assumption that does not always hold in real networks. We systematically analyze a recently-proposed restriction of PT that preserves node lifetimes (PTN), and a similar restriction (PTE) that also preserves edge lifetimes. We use PT, PTN, and PTE to characterize spreading dynamics on (i) synthetic networks with heterogeneous edge lifespans and tunable burstiness, and (ii) four real-world networks, including two in which nodes enter and leave the network dynamically. We find that predictions of spreading speed can change considerably with the choice of reference model. Moreover, the degree of disparity in the predictions reflects the extent of node/edge turnover, highlighting the importance of using lifetime-preserving reference models when nodes or edges are not continuously present in the network. Nature Publishing Group UK 2018-01-15 /pmc/articles/PMC5768694/ /pubmed/29335422 http://dx.doi.org/10.1038/s41598-017-18450-3 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Li, Mingwu
Rao, Vikyath D.
Gernat, Tim
Dankowicz, Harry
Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
title Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
title_full Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
title_fullStr Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
title_full_unstemmed Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
title_short Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
title_sort lifetime-preserving reference models for characterizing spreading dynamics on temporal networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768694/
https://www.ncbi.nlm.nih.gov/pubmed/29335422
http://dx.doi.org/10.1038/s41598-017-18450-3
work_keys_str_mv AT limingwu lifetimepreservingreferencemodelsforcharacterizingspreadingdynamicsontemporalnetworks
AT raovikyathd lifetimepreservingreferencemodelsforcharacterizingspreadingdynamicsontemporalnetworks
AT gernattim lifetimepreservingreferencemodelsforcharacterizingspreadingdynamicsontemporalnetworks
AT dankowiczharry lifetimepreservingreferencemodelsforcharacterizingspreadingdynamicsontemporalnetworks