研究人员开发了一个名为GraphNC的新框架，以改进半监督图异常检测。该方法通过利用标记和未标记数据来校准常态，并使用教师模型来指导过程。GraphNC结合了异常分数分布对齐和基于扰动的常态正则化，以提高异常分数和节点表示的准确性和可分离性。 AI
排序理由 这是一篇详细介绍图异常检测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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新框架利用标记和未标记数据校准图异常检测
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- 这是一篇详细介绍图异常检测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
- Source corroboration
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- Topics
- paper, other
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报道来源 [1]
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半监督图异常检测中的常态校准
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…