From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866929445281988608 |
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| author | Zheng, Yu Jin, Ming Liu, Yixin Chi, Lianhua Phan, Khoa T. Chen, Yi-Ping Phoebe |
| author_facet | Zheng, Yu Jin, Ming Liu, Yixin Chi, Lianhua Phan, Khoa T. Chen, Yi-Ping Phoebe |
| contents | Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), thus inevitably limiting their capability in capturing anomalous patterns in complex graph data. To address this limitation, we propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short). By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph. In maximizing the agreements between instances at both the patch and context levels concurrently, we estimate the anomaly score of each node with a statistical anomaly estimator according to the degree of agreement from multiple perspectives. To further exploit a handful of ground-truth anomalies (few-shot anomalies) that may be collected in real-life applications, we further propose an extended algorithm, ANEMONE-FS, to integrate valuable information in our method. We conduct extensive experiments under purely unsupervised settings and few-shot anomaly detection settings, and we demonstrate that the proposed method ANEMONE and its variant ANEMONE-FS consistently outperform state-of-the-art algorithms on six benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_05525 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach Zheng, Yu Jin, Ming Liu, Yixin Chi, Lianhua Phan, Khoa T. Chen, Yi-Ping Phoebe Machine Learning Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), thus inevitably limiting their capability in capturing anomalous patterns in complex graph data. To address this limitation, we propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short). By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph. In maximizing the agreements between instances at both the patch and context levels concurrently, we estimate the anomaly score of each node with a statistical anomaly estimator according to the degree of agreement from multiple perspectives. To further exploit a handful of ground-truth anomalies (few-shot anomalies) that may be collected in real-life applications, we further propose an extended algorithm, ANEMONE-FS, to integrate valuable information in our method. We conduct extensive experiments under purely unsupervised settings and few-shot anomaly detection settings, and we demonstrate that the proposed method ANEMONE and its variant ANEMONE-FS consistently outperform state-of-the-art algorithms on six benchmark datasets. |
| title | From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2202.05525 |