用扩散模型提升图异常检测，让无监督方法更精准。
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
- 基于扩散采样在隐空间注入判别性信息
- 在6个真实数据集上优于现有方法
- 适合需要高精度异常检测的场景
图异常检测（GAD）对于识别网络中的异常实体至关重要，受到多个领域广泛关注。传统无监督方法通过重构未标记数据的编码隐表示来检测异常，但常因无法捕捉关键判别性内容而导致性能不佳。为此，我们提出基于扩散模型的图异常检测器（DiffGAD）。其核心是一种新型隐空间学习范式，通过判别性内容引导，提升模型识别能力。该方法利用扩散采样向隐空间注入判别性信息，并引入内容保留机制，在不同尺度上保持有价值信息，显著提升异常识别效果，且时间与空间复杂度可控。我们在六个真实世界大规模数据集上进行了全面评估，采用多种指标验证了DiffGAD的卓越性能。
原文摘要 · Abstract (English)
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a reconstruction focus, often fail to capture critical discriminative content, leading to suboptimal anomaly detection. To address these challenges, we present a Diffusion-based Graph Anomaly Detector (DiffGAD). At the heart of DiffGAD is a novel latent space learning paradigm, meticulously designed to enhance its proficiency by guiding it with discriminative content. This innovative approach leverages diffusion sampling to infuse the latent space with discriminative content and introduces a content-preservation mechanism that retains valuable information across different scales, significantly improving its adeptness at identifying anomalies with limited time and space complexity. Our comprehensive evaluation of DiffGAD, conducted on six real-world and large-scale datasets with various metrics, demonstrated its exceptional performance.
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