Network point processes often exhibit latent structure that govern the behaviour of the sub-processes. It is not always reasonable to assume that this latent structure is static, and detecting when and how this driving structure changes is often of interest. In this paper, we introduce a novel online methodology for detecting changes within the latent structure of a network point process. We focus on block-homogeneous Poisson processes, where latent node memberships determine the rates of the edge processes. We propose a scalable variational procedure which can be applied on large networks in an online fashion via a Bayesian forgetting factor applied to sequential variational approximations to the posterior distribution. The proposed framework is tested on simulated and real-world data, and it rapidly and accurately detects changes to the latent edge process rates, and to the latent node group memberships, both in an online manner. In particular, in an application on the Santander Cycles bike-sharing network in central London, we detect changes within the network related to holiday periods and lockdown restrictions between 2019 and 2020.

翻译：网络点过程常表现出支配子过程行为的潜在结构。假设这种潜在结构是静态的并不总是合理的，检测这种驱动结构何时以及如何发生变化通常具有重要意义。本文提出了一种新颖的在线方法，用于检测网络点过程潜在结构内的变化。我们聚焦于块齐次泊松过程，其中潜在节点隶属关系决定了边过程的速率。我们提出了一种可扩展的变分推断方法，该方法通过对后验分布的序列变分近似应用贝叶斯遗忘因子，能够以在线方式应用于大型网络。所提出的框架在模拟数据和真实世界数据上进行了测试，能够快速准确地检测潜在边过程速率和潜在节点群组隶属关系的变化，且均以在线方式实现。特别地，在对伦敦市中心Santander Cycles共享单车网络的应用中，我们检测到了2019年至2020年间与假期时段和封锁限制相关的网络变化。