A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning

• URL: http://arxiv.org/abs/2607.11107v1
• Date: Mon, 13 Jul 2026 05:35:57 GMT
• Title: A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning
• Abstract summary: This paper proposes a novel framework for Graph Fraud Detection with Grouped completion and Confidence-aware Contrastive learning, named GFD-GC.<n>It first imitates heterogeneous neighborhood structures to implement group-wise aggregation, which obtains informative complete node features by capturing fine-grained graph contextual patterns.<n>Further, it introduces a confidence-aware supervised contrastive learning strategy to augment scarce labeled fraud nodes with high confidence pseudo-fraud nodes, which enhances the compactness of fraud representations and their separability from non-fraud nodes.
• Score: 5.183200614613901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection. However, the practical performance of existing GNN-based detectors is severely hindered by incomplete node attributes and extreme class imbalance within graphs. To mitigate these limitations, this paper proposes a novel framework for Graph Fraud Detection with Grouped attribute completion and Confidence-aware Contrastive learning, named GFD-GC. Specifically, it first imitates heterogeneous neighborhood structures to implement group-wise aggregation, which obtains informative complete node features by capturing fine-grained graph contextual patterns. Further, it introduces a confidence-aware supervised contrastive learning strategy to augment scarce labeled fraud nodes with high confidence pseudo-fraud nodes, which enhances the compactness of fraud representations and their separability from non-fraud nodes. Extensive experiments demonstrate the superiority of the proposed GFD-GC over state-of-the-art baselines on the graph fraud detection task, thereby providing an effective solution for real-world fraud scenarios.

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