用全局注意力检测大规模时序图异常，速度提升44倍
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
- 通过时空分块与层级变换器，实现端到端异常检测
- 在Elliptic数据集上准确率提升61%，训练时间减少44倍
- 适合处理超大规模时序图，开源代码可复现
尽管Transformer在多种数据上革新了机器学习，现有时序图Transformer仍存在感受野受限、子图提取开销大、泛化能力不足等问题。本文提出TGTOD，一种新型端到端时序图异常检测方法。TGTOD采用全局注意力建模时序图中的结构与时间依赖性。为应对可扩展性挑战，方法将大型时序图划分为时空块，再通过包含块变换器、聚类变换器和时间变换器的层次结构进行处理。我们在三个公开数据集上，在两种设置下评估TGTOD，并与多种基线对比。实验结果表明，TGTOD在Elliptic数据集上准确率（AP）提升61%；效率评估显示，相比现有时序图Transformer，训练时间减少44倍。为促进可复现性，代码已公开于https://github.com/kayzliu/tgtod。
原文摘要 · Abstract (English)
While Transformers have revolutionized machine learning on various data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead of subgraph extraction, and (3) suboptimal generalization capability beyond link prediction. In this paper, we rethink temporal graph Transformers and propose TGTOD, a novel end-to-end Temporal Graph Transformer for Outlier Detection. TGTOD employs global attention to model both structural and temporal dependencies within temporal graphs. To tackle scalability, our approach divides large temporal graphs into spatiotemporal patches, which are then processed by a hierarchical Transformer architecture comprising Patch Transformer, Cluster Transformer, and Temporal Transformer. We evaluate TGTOD on three public datasets under two settings, comparing with a wide range of baselines. Our experimental results demonstrate the effectiveness of TGTOD, achieving AP improvement of 61% on Elliptic. Furthermore, our efficiency evaluation shows that TGTOD reduces training time by 44x compared to existing Transformers for temporal graphs. To foster reproducibility, we make our implementation publicly available at https://github.com/kayzliu/tgtod.
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