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

Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert

Keywords: Dynamic Graph, Deep Generative Model, Network Time Series

TL;DR: We propose a novel method for generative modelling of dynamic networks.

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.

Type Of Submission: Full paper proceedings track submission (max 9 main pages).

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Type Of Submission: Full paper proceedings track submission.

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/damnets-a-deep-autoregressive-model-for/code)