ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
- Publisher:
- ACM
- Publication Type:
- Conference Proceeding
- Citation:
- International Conference on Information and Knowledge Management, Proceedings, 2021, pp. 3122-3126
- Issue Date:
- 2021-10-26
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 3459637.3482057.pdf | Published version | 1.61 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud detection. However, existing methods on graph anomaly detection usually consider the view in a single scale of graphs, which results in their limited capability to capture the anomalous patterns from different perspectives. Towards this end, we introduce a novel graph anomaly detection framework, namely ANEMONE, to simultaneously identify the anomalies in multiple graph scales. Concretely, ANEMONE first leverages a graph neural network backbone encoder with multi-scale contrastive learning objectives to capture the pattern distribution of graph data by learning the agreements between instances at the patch and context levels concurrently. Then, our method employs a statistical anomaly estimator to evaluate the abnormality of each node according to the degree of agreement from multiple perspectives. Experiments on three benchmark datasets demonstrate the superiority of our method.
Please use this identifier to cite or link to this item: