DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
DAMNETS:生成马尔可夫网络时间序列的深度自回归模型
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…