FoundAna：一种用于图异常检测的GNN辅助基础模型
FoundAna: A GNN-assisted Foundation Model for Graph Anomaly Detection
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- University of Nevada Las Vegas（内华达大学拉斯维加斯分校）
- University of Southern California（南加利福尼亚大学）
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中文总结 AI 辅助
FoundAna是首个结合GNN与Transformer的图异常检测基础模型，通过位置编码和重建误差实现跨图泛化，在九个基准数据集上优于现有方法。
中文摘要 AI 辅助
图异常检测旨在识别显著偏离预期模式的图结构（如节点、边或子图），这支持欺诈检测、垃圾邮件识别、网络入侵等关键应用。尽管该领域的方法不断增多，现有方法遵循每数据集一模型的范式，由于任务异质性、标签稀缺性和领域差异性，限制了它们在不同现实场景中的可迁移性。在这项工作中，我们引入了FoundAna，一种用于图异常检测的GNN辅助基础模型——首个专为可泛化的跨图异常检测而设计的基础模型框架，通过结合GNN和Transformer实现。FoundAna集成了一个针对异常检测的GNN组件和一个标准Transformer编码器，并增强了四种互补的位置编码，使模型能够捕获局部和全局结构信息。具体来说，经过位置编码增强的节点表示通过属性和邻接解码器传递，重建误差作为异常分数。在涵盖金融、社交和引文网络领域的九个基准数据集上的大量实验表明，FoundAna始终优于最先进的基线方法。代码实现和补充材料见此处：此https URL。
英文摘要
Graph anomaly detection aims to identify graph structures (e.g., nodes, edges, or subgraphs) that deviate significantly from expected patterns, which supports critical applications in fraud detection, spam identification, network intrusion, etc. Despite the growing methods in the field, existing approaches follow a one-model-per-dataset paradigm, limiting their transferability across diverse real-world scenarios due to task heterogeneity, label scarcity, and domain variability. In this work, we introduce FoundAna, a GNN-assisted Foundation Model for Graph Anomaly Detection - the first foundation model framework designated for generalizable, cross-graph anomaly detection by combining GNNs and transformers. FoundAna integrates an anomaly detection-specific GNN component with a standard transformer encoder augmented by four complementary positional encodings, which enable the model to capture both local and global structural information. Specifically, the positional encoding enriched node representations are passed through attribute and adjacency decoders, and the reconstruction errors serve as the anomaly score. Extensive experiments on nine benchmark datasets spanning financial, social, and citation network domains demonstrate that FoundAna consistently outperforms state-of-the-art baselines. The code implementation and Supplementary materials are here: https://github.com/FoundAna331/FoundAna.
