表格基础模型是强大的图异常检测器
Tabular Foundation Models are Strong Graph Anomaly Detectors
AI总结：
本文提出TFM4GAD，利用表格基础模型解决图异常检测问题，通过增强特征表和上下文学习提升跨领域泛化能力。
摘要：
图异常检测（GAD）旨在识别偏离多数的异常节点，已成为高风险网络领域越来越重要的问题。然而，现有GAD方法遵循“每个数据集一个模型”的范式，导致计算成本高、数据需求大，并且在转移到新数据集时泛化能力差。这需要一种基础模型，能够提供“一劳永逸”的GAD解决方案，能够在不同图中检测异常而无需重新训练。然而，由于领域间的结构和特征异质性较大，实现这一目标具有挑战性。在本文中，我们提出了TFM4GAD，一个简单而有效的框架，将表格基础模型（TFMs）适应于图异常检测。我们的关键洞察是，基础GAD的核心挑战，即处理异构特征、跨领域泛化以及在少量标签下操作，正是现代TFMs通过合成预训练和强大的上下文学习来解决的问题。因此，主要挑战在于结构：TFMs对图拓扑不敏感。TFM4GAD通过“扁平化”图，构建一个增强的特征表，该表通过拉普拉斯嵌入、局部和全局结构特征以及对异常敏感的邻域聚合来丰富原始节点特征。此增强的表格由TFM在完全上下文模式下处理。在多个数据集上进行的广泛实验表明，TFM4GAD在从头训练的专用GAD模型上取得了显著的性能提升。我们的工作提供了一种新的视角和实用的范式，利用TFMs作为强大且通用的图异常检测器。
英文摘要：
Graph anomaly detection (GAD), which aims to identify abnormal nodes that deviate from the majority, has become increasingly important in high-stakes Web domains. However, existing GAD methods follow a "one model per dataset" paradigm, leading to high computational costs, substantial data demands, and poor generalization when transferred to new datasets. This calls for a foundation model that enables a "one-for-all" GAD solution capable of detecting anomalies across diverse graphs without retraining. Yet, achieving this is challenging due to the large structural and feature heterogeneity across domains. In this paper, we propose TFM4GAD, a simple yet effective framework that adapts tabular foundation models (TFMs) for graph anomaly detection. Our key insight is that the core challenges of foundation GAD, handling heterogeneous features, generalizing across domains, and operating with scarce labels, are the exact problems that modern TFMs are designed to solve via synthetic pre-training and powerful in-context learning. The primary challenge thus becomes structural: TFMs are agnostic to graph topology. TFM4GAD bridges this gap by "flattening" the graph, constructing an augmented feature table that enriches raw node features with Laplacian embeddings, local and global structural characteristics, and anomaly-sensitive neighborhood aggregations. This augmented table is processed by a TFM in a fully in-context regime. Extensive experiments on multiple datasets with various TFM backbones reveal that TFM4GAD surprisingly achieves significant performance gains over specialized GAD models trained from scratch. Our work offers a new perspective and a practical paradigm for leveraging TFMs as powerful, generalist graph anomaly detectors.
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。
