Evaluating structural edge importance in temporal networks
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 l e $l_{e}$ —for the edges of a network. The metric is based on perturbing the adjacency...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-05-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-021-00279-6 |
| _version_ | 1818734433652441088 |
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| author | Isobel E. Seabrook Paolo Barucca Fabio Caccioli |
| author_facet | Isobel E. Seabrook Paolo Barucca Fabio Caccioli |
| author_sort | Isobel E. Seabrook |
| collection | DOAJ |
| description | 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 l e $l_{e}$ —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 l e $l_{e}$ . 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. |
| first_indexed | 2024-12-18T00:05:17Z |
| format | Article |
| id | doaj.art-49c3e3a658af4d27ac3e8a06ef0e7555 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-18T00:05:17Z |
| publishDate | 2021-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-49c3e3a658af4d27ac3e8a06ef0e75552022-12-21T21:27:50ZengSpringerOpenEPJ Data Science2193-11272021-05-0110112310.1140/epjds/s13688-021-00279-6Evaluating structural edge importance in temporal networksIsobel E. Seabrook0Paolo Barucca1Fabio Caccioli2Financial Conduct AuthorityDepartment of Computer Science, University College LondonDepartment of Computer Science, University College LondonAbstract 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 l e $l_{e}$ —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 l e $l_{e}$ . 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.https://doi.org/10.1140/epjds/s13688-021-00279-6Spectral perturbationDynamicsEdge predictabilityClassification |
| spellingShingle | Isobel E. Seabrook Paolo Barucca Fabio Caccioli Evaluating structural edge importance in temporal networks EPJ Data Science Spectral perturbation Dynamics Edge predictability Classification |
| title | Evaluating structural edge importance in temporal networks |
| title_full | Evaluating structural edge importance in temporal networks |
| title_fullStr | Evaluating structural edge importance in temporal networks |
| title_full_unstemmed | Evaluating structural edge importance in temporal networks |
| title_short | Evaluating structural edge importance in temporal networks |
| title_sort | evaluating structural edge importance in temporal networks |
| topic | Spectral perturbation Dynamics Edge predictability Classification |
| url | https://doi.org/10.1140/epjds/s13688-021-00279-6 |
| work_keys_str_mv | AT isobeleseabrook evaluatingstructuraledgeimportanceintemporalnetworks AT paolobarucca evaluatingstructuraledgeimportanceintemporalnetworks AT fabiocaccioli evaluatingstructuraledgeimportanceintemporalnetworks |