Networks with correlated edge processes
本文提出建模非平稳时间图过程的方法，结合时间序列与静态网络模型，分析医院接触网络中的交互数据，展示拟合方法在推断大量变量间相关性上的挑战。
Abstract This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependence 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 challenge in modelling and inferring correlation between a large number of variables.