New framework calibrates graph anomaly detection using labeled and unlabeled data

Researchers have developed a new framework called GraphNC to improve semi-supervised graph anomaly detection. This method calibrates normality by leveraging both labeled and unlabeled data, using a teacher model to guide the process. GraphNC incorporates anomaly score distribution alignment and perturbation-based normality regularization to enhance the accuracy and separability of anomaly scores and node representations. AI

RANK_REASON This is a research paper detailing a new framework for graph anomaly detection. [lever_c_demoted from research: ic=1 ai=1.0]

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• GraphNC
• Hezhe Qiao

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