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.

翻译：网络时间序列（也称为动态图）的生成模型在流行病学、生物学和经济学等领域具有巨大潜力，这些领域中以复杂图为基础的动力过程是核心研究对象。由于数据的高维性，以及同时需要表征时间依赖关系和边际网络结构，设计灵活且可扩展的生成模型是一项极具挑战性的任务。本文提出DAMNETS，一种可扩展的深度生成模型，用于网络时间序列。在真实数据集和合成数据集上，DAMNETS在我们所有样本质量评估指标上均优于对比方法。