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Evaluating structural edge importance in temporal networks

Seabrook, Isobel E., Barucca, Paolo and Caccioli, Fabio (2021) Evaluating structural edge importance in temporal networks. EPJ Data Science, 10 (1). ISSN 2193-1127

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Identification Number: 10.1140/epjds/s13688-021-00279-6

Abstract

To monitor risk in temporal financial networks, we need to understand how individual behaviours affect the global evolution of networks. Here we define a structural importance metric—which we denote as le—for the edges of a network. The metric is based on perturbing the adjacency matrix and observing the resultant change in its largest eigenvalues. We then propose a model of network evolution where this metric controls the probabilities of subsequent edge changes. We show using synthetic data how the parameters of the model are related to the capability of predicting whether an edge will change from its value of le. We then estimate the model parameters associated with five real financial and social networks, and we study their predictability. These methods have applications in financial regulation whereby it is important to understand how individual changes to financial networks will impact their global behaviour. It also provides fundamental insights into spectral predictability in networks, and it demonstrates how spectral perturbations can be a useful tool in understanding the interplay between micro and macro features of networks.

Item Type: Article
Official URL: https://epjdatascience.springeropen.com/
Additional Information: © 2021 The Authors
Divisions: Systemic Risk Centre
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences > HG Finance
Date Deposited: 27 Oct 2021 15:18
Last Modified: 17 Nov 2021 01:12
URI: http://eprints.lse.ac.uk/id/eprint/112515

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