DAMNETS: a deep autoregressive model for generating Markovian network time series
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...
| Autores principales: | , , , |
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| Formato: | Conference item |
| Lenguaje: | English |
| Publicado: |
Journal of Machine Learning Research
2022
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