Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913834453696512 |
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| author | Zong, Chang Zhuang, Yueting Shao, Jian Lu, Weiming |
| author_facet | Zong, Chang Zhuang, Yueting Shao, Jian Lu, Weiming |
| contents | Detecting anomalous edges in dynamic graphs is an important task in many applications over evolving triple-based data, such as social networks, transaction management, and epidemiology. A major challenge with this task is the absence of structural-temporal coupling information, which decreases the ability of the representation to distinguish anomalies from normal instances. Existing methods focus on handling independent structural and temporal features with embedding models, which ignore the deep interaction between these two types of information. In this paper, we propose a structural-temporal coupling anomaly detection architecture with a dynamic graph transformer model. Specifically, we introduce structural and temporal features from two integration levels to provide anomaly-aware graph evolutionary patterns. Then, a dynamic graph transformer enhanced by two-dimensional positional encoding is implemented to capture both discrimination and contextual consistency signals. Extensive experiments on six datasets demonstrate that our method outperforms current state-of-the-art models. Finally, a case study illustrates the strength of our method when applied to a real-world task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer Zong, Chang Zhuang, Yueting Shao, Jian Lu, Weiming Machine Learning Social and Information Networks 68T07, 68T09 Detecting anomalous edges in dynamic graphs is an important task in many applications over evolving triple-based data, such as social networks, transaction management, and epidemiology. A major challenge with this task is the absence of structural-temporal coupling information, which decreases the ability of the representation to distinguish anomalies from normal instances. Existing methods focus on handling independent structural and temporal features with embedding models, which ignore the deep interaction between these two types of information. In this paper, we propose a structural-temporal coupling anomaly detection architecture with a dynamic graph transformer model. Specifically, we introduce structural and temporal features from two integration levels to provide anomaly-aware graph evolutionary patterns. Then, a dynamic graph transformer enhanced by two-dimensional positional encoding is implemented to capture both discrimination and contextual consistency signals. Extensive experiments on six datasets demonstrate that our method outperforms current state-of-the-art models. Finally, a case study illustrates the strength of our method when applied to a real-world task. |
| title | Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer |
| topic | Machine Learning Social and Information Networks 68T07, 68T09 |
| url | https://arxiv.org/abs/2505.08330 |