AC2L-GAD: 基于主动反事实对比学习的图异常检测
AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection
AI总结：
AC2L-GAD 通过主动反事实对比学习方法，在图异常检测中有效解决标签稀缺和类别不平衡问题，提升检测性能。
摘要：
图异常检测旨在识别网络中的异常模式，但面临标签稀缺和极端类别不平衡的显著挑战。虽然图对比学习提供了一种有前途的无监督解决方案，但现有方法存在两个关键限制：随机增强破坏正对的语义一致性，而朴素的负采样会产生 trivial 且无信息的对比。我们提出 AC2L-GAD，一种基于主动反事实推理的主动对比学习框架，通过系统性的反事实推理解决这两个限制。通过结合信息论主动选择与反事实生成，我们的方法识别出结构复杂的节点，并生成保持异常的正增强以及提供困难对比的正常负对 counterparts，同时限制昂贵的反事实生成仅限于战略选择的子集。这种设计将计算开销减少了约 65%，同时保持检测质量。在九个基准数据集上进行实验，包括来自 GADBench 的现实世界金融交易图，结果表明 AC2L-GAD 在与最先进基线相比时表现出竞争力或优越性，在异常表现出复杂属性-结构交互的数据集上尤为明显。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
