DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
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DOI：
10.48550/arxiv.2203.15009
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
J. Clarkson;Mihai Cucuringu;Andrew Elliott;G. Reinert
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文献类型：
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作者：
J. Clarkson;Mihai Cucuringu;Andrew Elliott;G. Reinert
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，其中基于复杂图的动态是研究的核心对象。设计灵活和可扩展的生成模型是一项非常具有挑战性的任务，由于数据的高维性，以及需要表示时间依赖性和边缘网络结构。在这里，我们介绍DAMNETS，一个可扩展的网络时间序列深度生成模型。在真实的和合成数据集上，DAMNETS在我们所有的样本质量测量上都优于竞争方法。
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.