论文标题
网络时间序列中的欧几里得镜子和动态
Euclidean mirrors and dynamics in network time series
论文作者
论文摘要
分析网络演化的变化是统计网络推断的核心，这是由于预测和区分大流行诱导的组织和通信网络转变的最新挑战所强调。我们考虑了一个联合网络模型，其中每个节点具有相关的时间变化的低维潜在矢量的特征数据，并且连接概率是这些向量的函数。在温和的假设下，潜在载体的时间变化在适当的距离概念下表现出低维的歧管结构。该距离可以通过观察到的网络本身之间的分离度量来近似，并且在任何给定时间都存在以这种距离为特征的基础网络结构的欧几里得表示。这些称为欧几里得镜子的欧几里得表示允许可视化网络演化和转换网络推理问题，例如变更点和异常检测到经典环境。我们使用真实和合成数据来说明我们的方法，并确定与大型组织通信网络中大流行策略发生巨大变化相对应的变更点。
Analyzing changes in network evolution is central to statistical network inference, as underscored by recent challenges of predicting and distinguishing pandemic-induced transformations in organizational and communication networks. We consider a joint network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the time-varying evolution of the latent vectors exhibits low-dimensional manifold structure under a suitable notion of distance. This distance can be approximated by a measure of separation between the observed networks themselves, and there exist Euclidean representations for underlying network structure, as characterized by this distance, at any given time. These Euclidean representations, called Euclidean mirrors, permit the visualization of network evolution and transform network inference questions such as change-point and anomaly detection into a classical setting. We illustrate our methodology with real and synthetic data, and identify change points corresponding to massive shifts in pandemic policies in a communication network of a large organization.
