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

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...

Полное описание

Библиографические подробности
Главные авторы:	Clarkson, J, Cucuringu, M, Elliott, A, Reinert, G
Формат:	Conference item
Язык:	English
Опубликовано:	Journal of Machine Learning Research 2022

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