提出通用动态图异常检测方法，提升模型在不同场景下的适应能力。
A Generalizable Anomaly Detection Method in Dynamic Graphs
- 通过采样时序邻域图，分步提取结构与时间特征
- 在四个真实数据集上显著优于现有最佳方法
- 适合需要跨场景泛化的动态图异常检测任务
异常检测旨在识别数据中偏离正常模式的样本。该任务在动态图中尤为重要，因其常见于社交网络和网络安全等场景，具有结构演变和关系复杂的特点。尽管近期基于深度学习的方法在动态图异常检测上表现良好，但普遍缺乏泛化能力。本文提出 GeneralDyG，通过采样时序邻域图并依次提取结构与时间特征，解决泛化性面临的三大挑战：数据多样性、动态特征捕捉与计算成本。大量实验证明，GeneralDyG 在四个真实数据集上显著优于当前最优方法。
原文摘要 · Abstract (English)
Anomaly detection aims to identify deviations from normal patterns within data. This task is particularly crucial in dynamic graphs, which are common in applications like social networks and cybersecurity, due to their evolving structures and complex relationships. Although recent deep learning-based methods have shown promising results in anomaly detection on dynamic graphs, they often lack of generalizability. In this study, we propose GeneralDyG, a method that samples temporal ego-graphs and sequentially extracts structural and temporal features to address the three key challenges in achieving generalizability: Data Diversity, Dynamic Feature Capture, and Computational Cost. Extensive experimental results demonstrate that our proposed GeneralDyG significantly outperforms state-of-the-art methods on four real-world datasets.
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