用极端值理论分析图特征残差，提升动态图异常检测准确率。
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
- 通过时间序列分析图特征并提取残差，显式建模时序依赖。
- 在多个数据集上比TensorSplat和拉普拉斯异常检测准确率显著更高。
- 适合处理变规模图与复杂时序动态的异常检测场景。
在交通网络事故检测和计算机网络攻击识别等场景中，检测动态图中的异常行为具有重要意义。现有方法常面临高误报率、难以处理变规模图及复杂时序动态等问题。为此，本文提出一种新方法：首先通过时间序列分析大量相关图特征以显式建模时序依赖，再利用残差消除这些依赖；随后采用极端值理论对剩余极值进行稳健建模与分类，以降低误报率。在多个图实例上的对比实验表明，该方法在准确率上显著优于TensorSplat和拉普拉斯异常检测。
原文摘要 · Abstract (English)
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs can suffer from multiple limitations, such as high false positive rates as well as difficulties with handling variable-sized graphs and non-trivial temporal dynamics. To address this, we propose a technique where temporal dependencies are explicitly modelled via time series analysis of a large set of pertinent graph features, followed by using residuals to remove the dependencies. Extreme Value Theory is then used to robustly model and classify any remaining extremes, aiming to produce low false positives rates. Comparative evaluations on a multitude of graph instances show that the proposed approach obtains considerably better accuracy than TensorSplat and Laplacian Anomaly Detection.
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