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DAMNETS: a deep autoregressive model for generating Markovian network time series

Abstract:

Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study. Designing flexible and scalable generative models is a very challenging task due to the high dimensionality of the data, as well as the need to represent temporal dependencies and marginal network structure. Here we introduce DAMNETS, a scalable deep generative model for network t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
https://proceedings.mlr.press/v198/clarkson22a.html

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Merton College
Role:
Author
ORCID:
0000-0002-8464-2152
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Publisher:
Journal of Machine Learning Research
Host title:
Proceedings of the First Learning on Graphs Conference
Series:
Proceedings of Machine Learning Research
Series number:
198
Article number:
23
Pages:
23:1-23:19
Publication date:
2022-12-12
Acceptance date:
2022-11-24
Event title:
Learning on Graphs conference (LoG) 2022
Event location:
Online
Event website:
https://logconference.org/
Event start date:
2022-12-09
Event end date:
2022-12-12
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1317130
Local pid:
pubs:1317130
Deposit date:
2022-12-24

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