CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision

2025-08-17

AI Summary

Graph anomaly detection (GAD) faces critical challenges including severe scarcity of labeled anomalies, their inherent subtlety, and capacity for adversarial camouflage—leading to poor robustness in GNN training. To address these, we propose CRoC, a novel framework that leverages class-imbalance priors for context-aware reconstruction, decouples and encodes heterogeneous relational structures into message passing, and synergistically integrates contrastive learning with attribute recombination to jointly model limited labeled and abundant unlabeled data—thereby enhancing discriminability of node embeddings. Evaluated on seven real-world datasets, CRoC achieves up to 14% AUC improvement over state-of-the-art methods, with particularly pronounced gains under low-label regimes. It demonstrates superior capability in detecting camouflaged anomalies, effectively mitigating the impact of deceptive structural and attribute patterns.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Graph Neural Networks (GNNs) are widely used as the engine for various graph-related tasks, with their effectiveness in analyzing graph-structured data. However, training robust GNNs often demands abundant labeled data, which is a critical bottleneck in real-world applications. This limitation severely impedes progress in Graph Anomaly Detection (GAD), where anomalies are inherently rare, costly to label, and may actively camouflage their patterns to evade detection. To address these problems, we propose Context Refactoring Contrast (CRoC), a simple yet effective framework that trains GNNs for GAD by jointly leveraging limited labeled and abundant unlabeled data. Different from previous works, CRoC exploits the class imbalance inherent in GAD to refactor the context of each node, which builds augmented graphs by recomposing the attributes of nodes while preserving their interaction patterns. Furthermore, CRoC encodes heterogeneous relations separately and integrates them into the message-passing process, enhancing the model's capacity to capture complex interaction semantics. These operations preserve node semantics while encouraging robustness to adversarial camouflage, enabling GNNs to uncover intricate anomalous cases. In the training stage, CRoC is further integrated with the contrastive learning paradigm. This allows GNNs to effectively harness unlabeled data during joint training, producing richer, more discriminative node embeddings. CRoC is evaluated on seven real-world GAD datasets with varying scales. Extensive experiments demonstrate that CRoC achieves up to 14% AUC improvement over baseline GNNs and outperforms state-of-the-art GAD methods under limited-label settings.

Problem

• Detecting graph anomalies with limited labeled data
• Addressing class imbalance and adversarial camouflage in graphs
• Enhancing GNN robustness using contrastive learning on unlabeled data

Innovation

• Context refactoring contrast for limited supervision
• Augmented graphs preserving node interaction patterns
• Contrastive learning with heterogeneous relation integration