Higher-order temporal network effects through triplet evolution

Abstract We study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both ar...

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Autores principales:	Qing Yao, Bingsheng Chen, Tim S. Evans, Kim Christensen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d48cdadf2996423eb2078be22553364a
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spelling	oai:doaj.org-article:d48cdadf2996423eb2078be22553364a2021-12-02T16:06:42ZHigher-order temporal network effects through triplet evolution10.1038/s41598-021-94389-w2045-2322https://doaj.org/article/d48cdadf2996423eb2078be22553364a2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94389-whttps://doaj.org/toc/2045-2322Abstract We study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.Qing YaoBingsheng ChenTim S. EvansKim ChristensenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qing Yao Bingsheng Chen Tim S. Evans Kim Christensen Higher-order temporal network effects through triplet evolution
description	Abstract We study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.
format	article
author	Qing Yao Bingsheng Chen Tim S. Evans Kim Christensen
author_facet	Qing Yao Bingsheng Chen Tim S. Evans Kim Christensen
author_sort	Qing Yao
title	Higher-order temporal network effects through triplet evolution
title_short	Higher-order temporal network effects through triplet evolution
title_full	Higher-order temporal network effects through triplet evolution
title_fullStr	Higher-order temporal network effects through triplet evolution
title_full_unstemmed	Higher-order temporal network effects through triplet evolution
title_sort	higher-order temporal network effects through triplet evolution
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d48cdadf2996423eb2078be22553364a
work_keys_str_mv	AT qingyao higherordertemporalnetworkeffectsthroughtripletevolution AT bingshengchen higherordertemporalnetworkeffectsthroughtripletevolution AT timsevans higherordertemporalnetworkeffectsthroughtripletevolution AT kimchristensen higherordertemporalnetworkeffectsthroughtripletevolution
_version_	1718384933401001984

Higher-order temporal network effects through triplet evolution

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