平衡的异常引导自环图扩散模型用于归纳图异常检测
Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
AI总结：
本文提出一种平衡的异常引导自环图扩散模型，通过动态图建模和课程异常增强机制解决图异常检测中的动态性和类别不平衡问题。
摘要：
图异常检测（GAD）在欺诈检测和网络安全等应用中至关重要。尽管最近使用图神经网络（GNNs）取得了进展，但仍然存在两个主要挑战。在模型层面，大多数方法采用传递学习范式，假设静态图结构，使其不适合动态、演变的网络。在数据层面，极端的类别不平衡，即异常节点稀少，导致模型偏向，无法泛化到未见过的异常。这些挑战是相互关联的：静态传递框架限制了有效的数据增强，而不平衡加剧了归纳学习设置中的模型失真。为了解决这些挑战，我们提出了一种新的以数据为中心的框架，集成了动态图建模与平衡异常合成。我们的框架包括：（1）一个离散的自环图扩散模型，该模型捕捉异常的局部拓扑结构以生成与异常结构分布对齐的自环图；（2）一种课程异常增强机制，该机制在训练过程中动态调整合成数据的生成，专注于少数异常模式以提高检测和泛化能力。在五个数据集上的实验表明了我们框架的有效性。
英文摘要：
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.
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。
