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Cross-Domain Graph Level Anomaly Detection
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AI
DOI：10.1109/TKDE.2024.3462442.png)
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摘要
En 中文
Existing graph level anomaly detection methods are predominantly unsupervised due to high costs for obtaining labels, yielding sub-optimal detection accuracy when compared to supervised methods. Moreover, they heavily rely on the assumption that the training data exclusively consists of normal graphs. Hence, even the presence of a few anomalous graphs can lead to substantial performance degradation. To alleviate these problems, we propose a cross-domain graph level anomaly detection method, aiming to identify anomalous graphs from a set of unlabeled graphs (target domain) by using easily accessible normal graphs from a different but related domain (source domain). Our method consists of four components: a feature extractor that preserves semantic and topological information of individual graphs while incorporating the distance between different graphs; an adversarial domain classifier to make graph level representations domain-invariant; a one-class classifier to exploit label information in the source domain; and a class aligner to align classes from both domains based on pseudolabels. Experiments on seven benchmark datasets show that the proposed method largely outperforms state-of-the-art methods.
Keyword:
Anomaly detection
Feature extraction
Semantics
Databases
Training data
Training
Transfer learning
Graph anomaly detection
graph neural networks
graph transfer learning
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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