Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network's modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot's modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks by analyzing shifts in modularity. Moreover, we find our method is able to detect a real-world event within the \#Iran Twitter reply network, where each node has high-dimensional textual attributes.

翻译：在动态网络中检测异常行为始终是一项持续的挑战。当这些网络的基础拓扑结构受到个体高维节点属性的影响时，这一问题进一步加剧。我们通过追踪网络的模块度作为其社区结构的代理指标来解决此问题。我们利用图神经网络（GNNs）来估计每个快照的模块度。GNNs能够同时考虑网络结构和高维节点属性，为估计网络统计量提供了一种全面的方法。我们的方法通过仿真实验得到验证，这些实验证明了该方法能够通过分析模块度的变化来检测高属性网络中的变化。此外，我们发现我们的方法能够在#伊朗推特回复网络中检测到一个真实世界的事件，其中每个节点都具有高维文本属性。