AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection

• URL: http://arxiv.org/abs/2601.21171v1
• Date: Thu, 29 Jan 2026 02:11:56 GMT
• Title: AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection
• Authors: Kamal Berahmand, Saman Forouzandeh, Mehrnoush Mohammadi, Parham Moradi, Mahdi Jalili,
• Abstract summary: We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework.<n>We show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines.
• Score: 15.639397005651558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph anomaly detection aims to identify abnormal patterns in networks, but faces significant challenges from label scarcity and extreme class imbalance. While graph contrastive learning offers a promising unsupervised solution, existing methods suffer from two critical limitations: random augmentations break semantic consistency in positive pairs, while naive negative sampling produces trivial, uninformative contrasts. We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework that addresses both limitations through principled counterfactual reasoning. By combining information-theoretic active selection with counterfactual generation, our approach identifies structurally complex nodes and generates anomaly-preserving positive augmentations alongside normal negative counterparts that provide hard contrasts, while restricting expensive counterfactual generation to a strategically selected subset. This design reduces computational overhead by approximately 65% compared to full-graph counterfactual generation while maintaining detection quality. Experiments on nine benchmark datasets, including real-world financial transaction graphs from GADBench, show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines, with notable gains in datasets where anomalies exhibit complex attribute-structure interactions.

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