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weg2vec: Event embedding for temporal networks

Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when t...

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Detalles Bibliográficos
Autores principales: Torricelli, Maddalena, Karsai, Márton, Gauvin, Laetitia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189270/
https://www.ncbi.nlm.nih.gov/pubmed/32346033
http://dx.doi.org/10.1038/s41598-020-63221-2
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author Torricelli, Maddalena
Karsai, Márton
Gauvin, Laetitia
author_facet Torricelli, Maddalena
Karsai, Márton
Gauvin, Laetitia
author_sort Torricelli, Maddalena
collection PubMed
description Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when the network is varying in time. Temporal networks may provide an advantage in the description of real systems, but they code more complex information, which could be effectively represented only by a handful of methods so far. Here, we propose a new method of event embedding of temporal networks, called weg2vec, which builds on temporal and structural similarities of events to learn a low dimensional representation of a temporal network. This projection successfully captures latent structures and similarities between events involving different nodes at different times and provides ways to predict the final outcome of spreading processes unfolding on the temporal structure.
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spelling pubmed-71892702020-05-04 weg2vec: Event embedding for temporal networks Torricelli, Maddalena Karsai, Márton Gauvin, Laetitia Sci Rep Article Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when the network is varying in time. Temporal networks may provide an advantage in the description of real systems, but they code more complex information, which could be effectively represented only by a handful of methods so far. Here, we propose a new method of event embedding of temporal networks, called weg2vec, which builds on temporal and structural similarities of events to learn a low dimensional representation of a temporal network. This projection successfully captures latent structures and similarities between events involving different nodes at different times and provides ways to predict the final outcome of spreading processes unfolding on the temporal structure. Nature Publishing Group UK 2020-04-28 /pmc/articles/PMC7189270/ /pubmed/32346033 http://dx.doi.org/10.1038/s41598-020-63221-2 Text en © The Author(s) 2020 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
Torricelli, Maddalena
Karsai, Márton
Gauvin, Laetitia
weg2vec: Event embedding for temporal networks
title weg2vec: Event embedding for temporal networks
title_full weg2vec: Event embedding for temporal networks
title_fullStr weg2vec: Event embedding for temporal networks
title_full_unstemmed weg2vec: Event embedding for temporal networks
title_short weg2vec: Event embedding for temporal networks
title_sort weg2vec: event embedding for temporal networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189270/
https://www.ncbi.nlm.nih.gov/pubmed/32346033
http://dx.doi.org/10.1038/s41598-020-63221-2
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