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

Jase Clarkson, Mihai Cucuringu, Andrew Elliott and Gesine Reinert

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.

Date: 2022-03, Revised 2023-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-net
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