Tabular Foundation Models are Strong Graph Anomaly Detectors

2026-01-24

AI Summary

This work addresses the high computational cost and limited generalization of existing graph anomaly detection methods, which typically follow a “one model per dataset” paradigm. To overcome these limitations, we propose TFM4GAD, the first framework to leverage tabular foundation models for graph anomaly detection. Our approach flattens graph structures into enriched feature tables by integrating Laplacian embeddings, local and global structural features, and neighborhood aggregation, enabling context-aware inference with a tabular foundation model without retraining. This design facilitates zero-shot generalization across diverse graphs. Extensive experiments demonstrate that TFM4GAD significantly outperforms specialized graph anomaly detection models trained from scratch on multiple benchmarks, thereby validating the effectiveness and potential of tabular foundation models as universal detectors for graph anomalies.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Graph Anomaly Detection
• Foundation Model
• Tabular Foundation Models
• Generalization
• Structural Heterogeneity

Innovation

• Tabular Foundation Models
• Graph Anomaly Detection
• In-context Learning
• Structural Embedding
• Model Generalization