arXiv:2510.02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-supervised GAD, which assumes a subset of annotated normal nodes available during training, is among the most widely explored application settings. However, the normality learned by existing semi-supervised GAD methods is limited to the labeled normal nodes, often inclining to overfitting the given patterns. These can lead to high detection errors, such as high false positives. To overcome this limitat
The paper addresses a current limitation in semi-supervised graph anomaly detection, indicating ongoing academic efforts to refine AI performance in identifying anomalous patterns.
Improved graph anomaly detection can lead to more robust AI systems, enhancing cybersecurity, fraud detection, and general data integrity across various sectors.
The proposed 'Normality Calibration' method aims to make semi-supervised GAD more accurate by better generalizing from labeled normal data, reducing false positives caused by overfitting.
- · AI researchers
- · Cybersecurity sector
- · Financial services
- · Any industry relying on anomaly detection
- · Malicious actors
- · Systems with high false positive rates
More reliable anomaly detection systems will be deployed in critical infrastructure and financial networks.
Reduced false positives could decrease operational overhead and improve trust in AI-driven security measures.
As anomaly detection becomes more sophisticated, it could drive demand for more complex, adversarial AI methods to evade detection.
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Read at arXiv cs.LG