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New framework calibrates graph anomaly detection using labeled and unlabeled data

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework calibrates graph anomaly detection using labeled and unlabeled data

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guolei Zeng, Hezhe Qiao, Guoguo Ai, Jinsong Guo, Guansong Pang ·

    Normality Calibration in Semi-supervised Graph Anomaly Detection

    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 tra…

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