论文标题
Damnets：一种用于生成Markovian网络时间序列的深度自回旋模型
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
论文作者
论文摘要
网络时间序列的生成模型（也称为动态图）在流行病学，生物学和经济学等领域具有巨大的潜力，在这种领域中，基于图的复杂动力学是研究的核心对象。由于数据的高维度以及代表时间依赖性和边际网络结构的需求，设计灵活且可扩展的生成模型是一项非常具有挑战性的任务。在这里，我们介绍了DAMNets，这是网络时间序列的可扩展的深层生成模型。在真实和合成数据集上，该死的人在我们所有样本质量的措施上的表现都优于竞争方法。
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.
