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

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Listed:
  • Jase Clarkson
  • Mihai Cucuringu
  • Andrew Elliott
  • 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.

Suggested Citation

  • Jase Clarkson & Mihai Cucuringu & Andrew Elliott & Gesine Reinert, 2022. "DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series," Papers 2203.15009, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2203.15009
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