TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale

Kay Liu, Jiahao Ding, MohamadAli Torkamani, Philip S. Yu

Keywords: Graph Outlier Detection, Temporal Graph Learning, Graph Transformers

TL;DR: We propose TGTOD, an end-to-end temporal graph Transformer for outlier detection, conducting global spatiotemporal attention at scale.

Abstract:

Graph outlier detection aims to identify anomalous substructures in graphs that deviate significantly from normal patterns. Traditional methods primarily focus on static graphs, overlooking the dynamic nature of real-world networks and ignoring valuable temporal signals crucial for outlier detection. While Transformers have revolutionized machine learning on time-series 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 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 dataset. Furthermore, our efficiency evaluation shows that TGTOD reduces training time by 44×compared to existing Transformers for temporal graphs. To foster reproducibility, we make our implementation publicly available at https://anonymous.4open.science/r/tgtod.

Primary Area: learning on graphs and other geometries & topologies

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 4849