Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: i). how to compare graph snapshots across time, ii). how to capture temporal dependencies, and iii). how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short term and long term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that i). LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, ii). MultiLAD's advantage over contenders significantly increases when additional views are available, and iii). MultiLAD is highly robust to noise from individual views. In five real world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.

翻译：动态图是用于建模实体间随时间演化的复杂关系的丰富数据结构。其中，时态图异常检测对网络系统入侵识别、生态系统扰动检测及疫情暴发预警等实际应用至关重要。本文聚焦动态图变化点检测问题，攻克三大核心挑战：i) 如何跨时间比较图快照，ii) 如何捕获时间依赖性，iii) 如何融合时态图的多视图信息。为解决上述难题，我们首先提出拉普拉斯异常检测（LAD）方法，该方法将图拉普拉斯谱作为各快照图结构的低维嵌入表示，并采用双滑动窗口显式建模短期与长期依赖关系。继而提出MultiLAD——LAD在多视图图上的简洁高效泛化方法，首次实现多视图动态图的变化点检测。该方法通过标量幂平均运算聚合不同视图归一化图拉普拉斯的奇异值。大量合成实验表明：i) LAD与MultiLAD准确率显著超越各类最优基线和多视图扩展方法；ii) 随着视图数量增加，MultiLAD相较竞品的优势持续扩大；iii) MultiLAD对单视图噪声具有强鲁棒性。在五个真实动态图数据集上，我们验证了LAD与MultiLAD能将重大事件识别为顶级异常，例如影响多视图交通网络中人口流动的政府新冠干预措施实施。