论文标题
具有相关边缘过程的网络
Networks with Correlated Edge Processes
论文作者
论文摘要
本文提出了建模非组织时间图过程的方法。这对应于建模对边缘变量的观察（对象之间的关系），该变量指示了表现出依赖性（相关性）和相互作用的时间演变的节点（或对象）之间的相互作用。因此，本文将（Integer）时间序列模型与灵活的静态网络模型混合在一起，以生成时间图数据的模型，以及用于时变交互数据的统计拟合程序。我们通过分析医院接触网络来说明我们提出的拟合方法的功能，这显示了大量变量之间建模和推断相关性的高维数据挑战。
This article proposes methods to model nonstationary temporal graph processes. This corresponds to modelling the observation of edge variables (relationships between objects) indicating interactions between pairs of nodes (or objects) exhibiting dependence (correlation) and evolution in time over interactions. This article thus blends (integer) time series models with flexible static network models to produce models of temporal graph data, and statistical fitting procedures for time-varying interaction data. We illustrate the power of our proposed fitting method by analysing a hospital contact network, and this shows the high dimensional data challenge of modelling and inferring correlation between a large number of variables.
