Imperial College London
Alternate

DrTimEvans

Faculty of Natural Sciences, Department of Physics

Senior Lecturer
 
 
 
//

Contact

 

+44 (0)20 7594 7837t.evans Website

 
 
//

Assistant

 

Mrs Graziela De Nadai-Sowrey +44 (0)20 7594 7843

 
//

Location

 

609Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Yao:2021:10.1038/s41598-021-94389-w,
author = {Yao, Q and Evans, T and Chen, B and Christensen, KIM},
doi = {10.1038/s41598-021-94389-w},
journal = {Scientific Reports},
pages = {1--17},
title = {Higher-order temporal network effects through triplet evolution},
url = {http://dx.doi.org/10.1038/s41598-021-94389-w},
volume = {11},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We study the evolution of networks through ‘triplets’ — three-node graphlets. We develop a method to compute a transitionmatrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions inthe 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 demonstratethat 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 ouralgorithm’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 wefind our method, along with two other methods based on non-local interactions, give the best overall performance. Theresults also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understandand predict the evolution of different real-world systems.
AU  - Yao,Q
AU  - Evans,T
AU  - Chen,B
AU  - Christensen,KIM
DO  - 10.1038/s41598-021-94389-w
EP  - 17
PY  - 2021///
SN  - 2045-2322
SP  - 1
TI  - Higher-order temporal network effects through triplet evolution
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-021-94389-w
UR  - https://www.nature.com/articles/s41598-021-94389-w
UR  - http://hdl.handle.net/10044/1/90849
VL  - 11
ER  -
Download