Conference item
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...
Expand abstract
- Publication status:
- Accepted
- Peer review status:
- Peer reviewed
Actions
Authors
Bibliographic Details
- Publisher:
- ML Research Press Publisher's website
- Host title:
- Proceedings of Machine Learning Research
- Series:
- Learning on Graphs Conference
- 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
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1317130
- Local pid:
- pubs:1317130
- Deposit date:
- 2022-12-24
Terms of use
- Notes:
- This paper has been accepted for at the Learning on Graphs Conference (LoG) 2022.
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record