走向边界推理：一种基于Mixup的图异常检测方法
Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection
AI总结：
ANOMIX通过混合生成硬负样本提升图异常检测的推理能力，有效识别边界异常。
摘要：
尽管基于GNN的检测方法在识别明显异常方面表现出色，但它们往往在处理边界异常方面遇到困难——这些异常节点被巧妙伪装，难以与正常实例区分开来。这种局限性突显了现有方法在推理能力上的根本性差距。我们归因于标准图对比学习（GCL）对简单负样本的依赖，这种依赖促进了简单决策边界的形成。为了解决这个问题，我们提出了ANOMIX，一种通过线性插值正常和异常子图的表示来合成信息丰富硬负样本的框架。这种图混合策略有意在决策边界中填充难以检测的样本。通过有针对性的实验分析，我们证明ANOMIX能够成功分离这些边界异常，这些异常在最先进基线方法中无法区分，如这些挑战性案例的分数分布显示出明显差异。这些发现表明，通过混合合成硬负样本是优化GNN表示空间的有效策略，从而增强其推理能力，以实现更稳健和可靠的图异常检测。代码可在https://github.com/missinghwan/ANOMIX上获得。
英文摘要：
While GNN-based detection methods excel at identifying overt outliers, they often struggle with boundary anomalies -- subtly camouflaged nodes that are difficult to distinguish from normal instances. This limitation highlights a fundamental gap in the reasoning capabilities of existing methods. We attribute this issue to the reliance of standard Graph Contrastive Learning (GCL) on easy negatives, which fosters the learning of simplistic decision boundaries. To address this issue, we propose ANOMIX, a framework that synthesizes informative hard negatives by linearly interpolating representations of normal and abnormal subgraphs. This graph mixup strategy intentionally populates the decision boundary with hard-to-detect samples. Through targeted experimental analysis, we demonstrate that ANOMIX successfully separates these boundary anomalies where state-of-the-art baselines fail, as shown by a clear distinction in the score distributions for these challenging cases. These findings suggest that synthesizing hard negatives via mixup is a potent strategy for refining GNN representation space, which in turn enhances its reasoning capacity for more robust and reliable graph anomaly detection. Code is available at https://github.com/missinghwan/ANOMIX.
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。
