FracAug通过生成语义一致的图变体提升小样本异常检测性能
FracAug: Fractional Augmentation boost Graph-level Anomaly Detection under Limited Supervision
- 基于加权距离感知损失生成多尺度图变体，避免数据不平衡影响
- 利用原图与增强图相互验证伪标签，迭代扩充训练集
- 兼容多种GNN模型，在12个数据集上平均提升5.7%以上准确率
图级异常检测在药物发现等场景中至关重要，但高标注成本与数据不平衡严重制约图神经网络（GNN）性能。为此，本文提出FracAug，一种可插拔的增强框架，通过生成语义一致的图变体并利用互验证进行伪标注来增强GNN。不同于以往启发式方法，FracAug学习图内语义，基于新型加权距离感知边界损失生成分数化变体，捕捉多尺度拓扑结构，生成多样化且语义保持的图，不受数据不平衡影响。随后，利用原始图与增强图的预测结果对未标记数据进行伪标注，迭代扩展训练集。作为与模型无关的模块，FracAug展现出显著通用性与有效性：在12个真实世界数据集上对14种GNN进行实验，平均AUROC、AUPRC和F1-score分别提升最高达5.72%、7.23%和4.18%。
原文摘要 · Abstract (English)
Graph-level anomaly detection (GAD) is critical in diverse domains such as drug discovery, yet high labeling costs and dataset imbalance hamper the performance of Graph Neural Networks (GNNs). To address these issues, we propose FracAug, an innovative plug-in augmentation framework that enhances GNNs by generating semantically consistent graph variants and pseudo-labeling with mutual verification. Unlike previous heuristic methods, FracAug learns semantics within given graphs and synthesizes fractional variants, guided by a novel weighted distance-aware margin loss. This captures multi-scale topology to generate diverse, semantic-preserving graphs unaffected by data imbalance. Then, FracAug utilizes predictions from both original and augmented graphs to pseudo-label unlabeled data, iteratively expanding the training set. As a model-agnostic module compatible with various GNNs, FracAug demonstrates remarkable universality and efficacy: experiments across 14 GNNs on 12 real-world datasets show consistent gains, boosting average AUROC, AUPRC, and F1-score by up to 5.72%, 7.23%, and 4.18%, respectively.
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