GraphNC: A Framework for Improved Graph Anomaly Detection Through Normality Calibration
Researchers propose GraphNC, a new framework for semi-supervised graph anomaly detection that improves upon existing methods by calibrating normality using both labeled and unlabeled data. The method addresses overfitting limitations in current approaches by aligning anomaly scores and regularizing node representations through two main components. This advancement is significant because graph anomaly detection is critical for identifying irregular patterns across diverse applications, and reducing false positives improves practical deployment.
GraphNC is a graph normality calibration framework designed to enhance semi-supervised graph anomaly detection (GAD), which identifies irregular patterns in graph-structured data using a subset of labeled normal nodes. The framework addresses a key limitation of existing methods: they tend to overfit to labeled normal nodes, resulting in high false positive rates. GraphNC operates by leveraging both labeled and unlabeled data to calibrate normality from a teacher model (a pre-trained semi-supervised GAD model) across two spaces: anomaly score and node representation. The framework comprises two main components: anomaly score distribution alignment (ScoreDA), which aligns model scores with the teacher model's distribution to create more separable anomaly scores, and perturbation-based normality regularization (NormReg), which regularizes graph normality in representation space to make normal node representations more compact while mitigating misleading scores from the teacher model.
What's missing
The paper does not provide empirical results, benchmarks, or comparative performance metrics against existing semi-supervised GAD methods. Specific datasets used for evaluation, quantitative improvements in false positive/negative rates, and computational complexity analysis are not included in the abstract.
What different sources said
- arXiv cs.LGCenter
Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense
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