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:动态图形是丰富的数据结构,用来建模实体间长期的复杂关系。 特别是, 时间图形中的异常检测对于许多真实世界应用至关重要, 如网络系统中的入侵识别、 生态系统扰动的检测和流行病爆发的检测。 在本文中, 我们侧重于动态图形中的变化点检测, 并解决与这一问题相关的三大挑战 : 一. 如何对不同时间的图形进行对比图形截图, 二. 如何捕捉时间图的不同观点。 为了解决上述挑战, 我们首先提议 Laplaceian Anomaly 检测(LAD), 以图的频谱为网络, 将Laplacian 检测(LAD) 用作每个快照中图形结构的低维度嵌入。 在动态图表中, LAD 明确以两个滑动窗口为短期和长期的模型, 简单而有效地将LAD 概括到多视图图中。 多视角中, 我们从不同视角的Laplace- 19 的多维(L) 图像中, 从不同观点的多维(L) 的多维(我们从不同观点中) 的多维(LAD) 的多维(OD) 的多维(O) 的多维) 的多维(O) 展示, 展示, 展示, 展示, 的动态) 以不同的图像中, 以 的动态的动态的动态的动态的动态的动态的动态的动态的大规模的动态(以 的动态的动态的动态的动态的动态 显示的动态的动态的动态) 显示的大规模的动态的动态的动态的动态的动态 。