Higher-order temporal network effects through triplet evolution
File(s)
Published version
Author(s)
Yao, Qing
Evans, Tim
Chen, Bingsheng
Christensen, KIM
Type
Journal Article
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.
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.
Editor(s)
Bianconi, Ginestra
Date Issued
2021-07-29
Date Acceptance
2021-07-08
Citation
Scientific Reports, 2021, 11, pp.1-17
ISSN
2045-2322
Publisher
Nature Publishing Group
Start Page
1
End Page
17
Journal / Book Title
Scientific Reports
Volume
11
Copyright Statement
© The Author(s) 2021. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.nature.com/articles/s41598-021-94389-w
Subjects
Network Meta-Analysis
Machine Learning
TEMPORAL NETWORKS
Date Publish Online
2021-07-29