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CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection

arXiv 2504.02248 ↗First posted 3 Apr 2025cs.LGcited by 0

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Abstract (arXiv)

Graph Anomaly Detection (GAD) is critical in security-sensitive domains, yet faces reliability challenges: miscalibrated confidence estimation (underconfidence in normal nodes, overconfidence in anomalies), adversarial vulnerability of derived confidence score under structural perturbations, and limited efficacy of conventional calibration methods for sparse anomaly patterns. Thus we propose CRC-SGAD, a framework integrating statistical risk control into GAD via two innovations: (1) A Dual-Threshold Conformal Risk Control mechanism that provides theoretically guaranteed bounds for both False Negative Rate (FNR) and False Positive Rate (FPR) through providing prediction sets; (2) A Subgraph-aware Spectral Graph Neural Calibrator (SSGNC) that optimizes node representations through adaptive spectral filtering while reducing the size of prediction sets via hybrid loss optimization. Experiments on four datasets and five GAD models demonstrate statistically significant improvements in FNR and FPR control and prediction set size. CRC-SGAD establishes a paradigm for statistically rigorous anomaly detection in graph-structured security applications.

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References · 21

  1. 2020Classification with Valid and Adaptive Coveragecited by 390
  2. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  3. 2022Conformal Risk Controlcited by 290
  4. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  5. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  6. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  7. 2022Rethinking Graph Neural Networks for Anomaly Detectioncited by 185
  8. 2021Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibrationcited by 91
  9. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 88
  10. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  11. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
  12. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
9 more
  1. 2016Hiding Individuals and Communities in a Social Networkcited by 77
  2. 2018Fast Gradient Attack on Network Embeddingcited by 65
  3. 2022DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detectioncited by 63
  4. 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detectioncited by 57
  5. 2023Attacking Fake News Detectors via Manipulating News Social Engagementcited by 23
  6. 2024Partitioning Message Passing for Graph Fraud Detectioncited by 21
  7. 2024Uncertainty in Graph Neural Networks: A Surveycited by 20
  8. 2024A General Black-box Adversarial Attack on Graph-based Fake News Detectorscited by 11
  9. 2023Towards Reliable Rare Category Analysis on Graphs via Individual Calibrationcited by 6

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