Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection

• URL: http://arxiv.org/abs/2602.05232v1
• Date: Thu, 05 Feb 2026 02:46:54 GMT
• Title: Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
• Authors: Chunyu Wei, Siyuan He, Yu Wang, Yueguo Chen, Yunhai Wang, Bing Bai, Yidong Zhang, Yong Xie, Shunming Zhang, Fei Wang,
• Abstract summary: Graph anomaly detection is crucial in applications like fraud detection and cybersecurity.<n>We propose a novel data-centric framework that integrates dynamic graph modeling with balanced anomaly synthesis.<n>Our framework features: (1) a discrete ego-graph diffusion model, which captures the local topology of anomalies to generate ego-graphs aligned with anomalous structural distribution, and (2) a curriculum anomaly augmentation mechanism, which dynamically adjusts synthetic data generation during training, focusing on underrepresented anomaly patterns to improve detection and generalization.
• Score: 20.567053994822867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenges persist. At the model level, most methods adopt a transductive learning paradigm, which assumes static graph structures, making them unsuitable for dynamic, evolving networks. At the data level, the extreme class imbalance, where anomalous nodes are rare, leads to biased models that fail to generalize to unseen anomalies. These challenges are interdependent: static transductive frameworks limit effective data augmentation, while imbalance exacerbates model distortion in inductive learning settings. To address these challenges, we propose a novel data-centric framework that integrates dynamic graph modeling with balanced anomaly synthesis. Our framework features: (1) a discrete ego-graph diffusion model, which captures the local topology of anomalies to generate ego-graphs aligned with anomalous structural distribution, and (2) a curriculum anomaly augmentation mechanism, which dynamically adjusts synthetic data generation during training, focusing on underrepresented anomaly patterns to improve detection and generalization. Experiments on five datasets demonstrate that the effectiveness of our framework.

Related papers

• Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer [41.16574023720132]
  We propose a structural-temporal coupling anomaly detection architecture with a dynamic graph transformer model.<n>Specifically, we introduce structural and temporal features from two integration levels to provide anomaly-aware graph evolutionary patterns.
  arXiv  Detail & Related papers  (2025-05-13T08:10:41Z)
• Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning [1.3756846638796]
  We propose an imbalanced GLAD method via counterfactual augmentation and feature learning. We apply the model to brain disease datasets, which can prove the capability of our work.
  arXiv  Detail & Related papers  (2024-07-13T13:40:06Z)
• ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection [84.0718034981805]
  We introduce a novel framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD) In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. In the next stage, the decoders are retrained for detection on the original graph.
  arXiv  Detail & Related papers  (2023-12-22T09:02:01Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• GADY: Unsupervised Anomaly Detection on Dynamic Graphs [18.1896489628884]
  We propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets.
  arXiv  Detail & Related papers  (2023-10-25T05:27:45Z)
• Provable Tensor Completion with Graph Information [49.08648842312456]
  We introduce a novel model, theory, and algorithm for solving the dynamic graph regularized tensor completion problem. We develop a comprehensive model simultaneously capturing the low-rank and similarity structure of the tensor. In terms of theory, we showcase the alignment between the proposed graph smoothness regularization and a weighted tensor nuclear norm.
  arXiv  Detail & Related papers  (2023-10-04T02:55:10Z)
• Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting [60.03169701753824]
  We propose a novel Dynamic Diffusion-al Graph Neural Network (DVGNN) fortemporal forecasting. The proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result.
  arXiv  Detail & Related papers  (2023-05-16T11:38:19Z)
• Graph Generation with Diffusion Mixture [57.78958552860948]
  Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. We propose a generative framework that models the topology of graphs by explicitly learning the final graph structures of the diffusion process.
  arXiv  Detail & Related papers  (2023-02-07T17:07:46Z)
• Homophily modulates double descent generalization in graph convolution networks [33.703222768801574]
  We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. We use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets.
  arXiv  Detail & Related papers  (2022-12-26T09:57:09Z)
• DAGAD: Data Augmentation for Graph Anomaly Detection [57.92471847260541]
  This paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics.
  arXiv  Detail & Related papers  (2022-10-18T11:28:21Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Anomaly Detection in Dynamic Graphs via Transformer [30.926884264054042]
  We present a novel Transformer-based Anomaly Detection framework for DYnamic graph (TADDY) Our framework constructs a comprehensive node encoding strategy to better represent each node's structural and temporal roles in an evolving graphs stream. Our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on four real-world datasets.
  arXiv  Detail & Related papers  (2021-06-18T02:27:19Z)

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.