GFM4GA: Graph Foundation Model for Group Anomaly Detection

• URL: http://arxiv.org/abs/2601.10193v1
• Date: Thu, 15 Jan 2026 08:48:34 GMT
• Title: GFM4GA: Graph Foundation Model for Group Anomaly Detection
• Authors: Jiujiu Chen, Weijun Zeng, Shaofeng Hu, Sihong Xie, Hui Xiong,
• Abstract summary: Group anomaly detection is crucial in many network applications.<n>We propose GFM4GA, a novel graph foundation model for group anomaly detection.<n>Experiments show that GFM4GA surpasses group anomaly detectors and GFMs for individual anomalies.
• Score: 20.29091180610742
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Group anomaly detection is crucial in many network applications, but faces challenges due to diverse anomaly patterns. Motivated by the success of large language models (LLMs) in natural language processing, graph foundation models (GFMs) is proposed to handle few-shot learning task with fewer labeling efforts. GFMs have been successfully applied to detection of individual anomalies but cannot be generalized to group anomalies, as group anomaly patterns must be detected as a whole and individuals in an abnormal group can look rather normal. Therefore, we propose GFM4GA, a novel graph foundation model for group anomaly detection. The pipeline is pretrained via dual-level contrastive learning based on feature-based estimation and group extraction, to capture potential group anomaly structure and feature inconsistencies. In the downstream tasks, the pipeline is finetuned in parameter-constrained and group-anomaly-proportion weighted few-shot settings, and its adaptive ability to unseen group anomalies expanded via group contexts determined by labeled anomaly neighbors. Experiments show that GFM4GA surpasses group anomaly detectors and GFMs for individual anomalies, achieving average improvements of 2.85% in AUROC and 2.55% in AUPRC.

Related papers

• Tabular Foundation Models are Strong Graph Anomaly Detectors [18.257503243010436]
  Graph anomaly detection (GAD) aims to identify abnormal nodes that deviate from the majority.<n>Existing GAD methods follow a "one model per dataset" paradigm.<n>This calls for a foundation model that enables a "one-for-all" GAD solution.
  arXiv  Detail & Related papers  (2026-01-24T04:19:45Z)
• Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time [60.341117019125214]
  We propose a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns in graph anomaly detection (GAD)<n>To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level.<n>Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
  arXiv  Detail & Related papers  (2025-11-10T12:10:05Z)
• Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection [53.137651284042434]
  Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples limits the effectiveness of existing methods.<n>We propose Generate grained Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework.<n>GAA generates realistic, diverse, and semantically aligned anomalies using only a small number of samples.
  arXiv  Detail & Related papers  (2025-07-13T12:56:59Z)
• Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection [59.042018542376596]
  Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors.<n>Anomaly-Aware Pre-Training and Fine-Tuning (APF) is a framework to mitigate the challenges in GAD.<n> Comprehensive experiments on 10 benchmark datasets validate the superior performance of APF in comparison to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-04-19T09:57:35Z)
• AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection [22.584555292512427]
  AnomalyGFM is a graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD.<n>We show that AnomalyGFM significantly outperforms state-of-the-art competing methods under both zero- and few-shot GAD settings.
  arXiv  Detail & Related papers  (2025-02-13T12:10:05Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly.<n> equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset.<n>Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Alleviating Structural Distribution Shift in Graph Anomaly Detection [70.1022676681496]
  Graph anomaly detection (GAD) is a challenging binary classification problem. Gallon neural networks (GNNs) benefit the classification of normals from aggregating homophilous neighbors. We propose a framework to mitigate the effect of heterophilous neighbors and make them invariant.
  arXiv  Detail & Related papers  (2024-01-25T13:07:34Z)
• Open-Set Graph Anomaly Detection via Normal Structure Regularisation [30.638274744518682]
  Open-set Graph Anomaly Detection (GAD) aims to train a detection model using a small number of normal and anomaly nodes.<n>Current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes.<n>We propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to achieve generalised detection ability to unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-12T13:25:28Z)
• Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach [25.383587951822964]
  This paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD) The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups.
  arXiv  Detail & Related papers  (2023-08-02T10:22:04Z)
• GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories [0.9971221656644376]
  Group Anomaly Detection (GAD) identifies unusual pattern in groups where individual members might not be anomalous. This paper introduces GADformer, a BERT-based model for attention-driven GAD on trajectories in unsupervised and semi-supervised settings. We also introduce the Block-Attention-anomaly-Score (BAS) to enhance model transparency by scoring attention patterns.
  arXiv  Detail & Related papers  (2023-03-17T08:49:09Z)
• AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization [109.91265884632239]
  Group distributionally robust optimization (G-DRO) can minimize the worst-case loss over a set of pre-defined groups over training data. We propose AGRO -- Adversarial Group discovery for Distributionally Robust Optimization. AGRO results in 8% higher model performance on average on known worst-groups, compared to prior group discovery approaches.
  arXiv  Detail & Related papers  (2022-12-02T00:57:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.