DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

Jase Clarkson; Mihai Cucuringu; Andrew Elliott; Gesine Reinert

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arXiv · 2203.15009

DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

Jase Clarkson·Mihai Cucuringu·Andrew Elliott·Gesine Reinert

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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 time series. DAMNETS outperforms competing methods on all of our measures of sample quality, over both real and synthetic data sets.

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BibTeX RIS

Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert. 2022-03-28. DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series. https://arxiv.org/abs/2203.15009

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