综述图异常检测的泛化能力，从迁移学习到通用模型
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
- 梳理图异常检测泛化方法演进，构建细粒度分类体系
- 提出统一问题设定，系统回顾现有跨场景泛化技术
- 适合关注图神经网络泛化与实际应用落地的研究者
图异常检测（GAD）近年来受到广泛关注，用于在社交网络、电商等图结构应用中识别恶意样本。然而，多数GAD方法假设训练与测试分布一致，且针对特定任务定制，难以适应真实场景中的数据分布漂移和新应用中样本稀缺的问题。为此，近期研究聚焦于通过迁移学习利用相关领域知识提升检测性能，或开发可泛化于多场景的GAD基础模型。由于对GAD泛化尚缺乏系统理解，本文提供全面综述：首先追溯泛化在GAD中的演进历程，形式化问题设定，并构建系统性分类体系；基于该细粒度分类，对现有泛化GAD方法进行最新、全面的回顾；最后，指出当前开放挑战并提出未来方向，以推动该新兴领域的发展。
原文摘要 · Abstract (English)
Graph anomaly detection (GAD) has attracted increasing attention in recent years for identifying malicious samples in a wide range of graph-based applications, such as social media and e-commerce. However, most GAD methods assume identical training and testing distributions and are tailored to specific tasks, resulting in limited adaptability to real-world scenarios such as shifting data distributions and scarce training samples in new applications. To address the limitations, recent work has focused on improving the generalization capability of GAD models through transfer learning that leverages knowledge from related domains to enhance detection performance, or developing "one-for-all" GAD foundation models that generalize across multiple applications. Since a systematic understanding of generalization in GAD is still lacking, in this paper, we provide a comprehensive review of generalization in GAD. We first trace the evolution of generalization in GAD and formalize the problem settings, which further leads to our systematic taxonomy. Rooted in this fine-grained taxonomy, an up-to-date and comprehensive review is conducted for the existing generalized GAD methods. Finally, we identify current open challenges and suggest future directions to inspire future research in this emerging field.
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