用于异构图异常检测的卡方小波图神经网络
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
AI总结：
针对异构图异常检测面临的挑战，提出基于卡方滤波器的光谱GNN框架ChiGAD，包含多图卡方滤波器、交互式元图卷积和贡献知情交叉熵损失，实验表明其在多指标上优于现有模型，同构变体ChiGNN也表现出色。
摘要：
异质网络中的图异常检测（GAD）由于节点和边的异质性而面临独特挑战。现有图神经网络（GNN）方法主要关注同构图异常检测，无法解决三个关键问题：跨不同元路径捕获异常信号和丰富语义；在HIN维度对齐中保留高频内容；从类别不平衡的困难异常样本中有效学习。为克服这些问题，我们提出了基于新颖卡方滤波器的光谱GNN框架ChiGAD。具体包括多图卡方滤波器、交互式元图卷积和贡献知情交叉熵损失。在公共和工业数据集上的大量实验表明，ChiGAD在多个指标上优于现有模型。其同构变体ChiGNN在七个GAD数据集上表现出色，验证了卡方滤波器的有效性。
英文摘要：
Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus on homogeneous GAD and thus fail to address three key issues: (C1) Capturing abnormal signal and rich semantics across diverse meta-paths; (C2) Retaining high-frequency content in HIN dimension alignment; and (C3) Learning effectively from difficult anomaly samples with class imbalance. To overcome these, we propose ChiGAD, a spectral GNN framework based on a novel Chi-Square filter, inspired by the wavelet effectiveness in diverse domains. Specifically, ChiGAD consists of: (1) Multi-Graph Chi-Square Filter, which captures anomalous information via applying dedicated Chi-Square filters to each meta-path graph; (2) Interactive Meta-Graph Convolution, which aligns features while preserving high-frequency information and incorporates heterogeneous messages by a unified Chi-Square Filter; and (3) Contribution-Informed Cross-Entropy Loss, which prioritizes difficult anomalies to address class imbalance. Extensive experiments on public and industrial datasets show that ChiGAD outperforms state-of-the-art models on multiple metrics. Additionally, its homogeneous variant, ChiGNN, excels on seven GAD datasets, validating the effectiveness of Chi-Square filters. Our code is available at https://github.com/HsipingLi/ChiGAD.
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