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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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 |
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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 |
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