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FIAD: Graph anomaly detection framework based feature injection
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DOI：10.1016/j.eswa.2024.125216.png)
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摘要
En 中文
Anomaly detection on attributed networks is widely used in the real world, such as in spam, and fraud detection. Due to the scarcity of anomaly data in the real world, anomaly detection methods typically use unsupervised techniques to detect anomalies. In recent years, methods based on graph neural networks and autoencoders have performed significantly better than previous methods with injected anomalies. But the current node-based injection methods increase the abnormality of some nodes, which is not conducive to learning more fine-grained anomalous knowledge, appealing to build a more general-purpose anomaly detector. In this paper, we propose a dimension-based method which injects anomalies into feature information by dimension: Feature-Injected Anomaly Detection framework (FIAD). A unique loss function and algorithmic process are developed that significantly enhance the performance by injecting features. Experimental evaluations on seven datasets, comprising four benchmark and three real-world datasets, demonstrate that our proposed method outperforms previous approaches, achieving state-of-the-art results.
Keyword:
Graph neural networks
Anomaly detection
Deep learning
Self-supervised learning
Feature injection
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
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