从拓扑学习到图生成：一个统一视角
From topology learning to graph generation: A unifying perspective
- University of Oxford（牛津大学）
- The Chinese University of Hong Kong（香港中文大学）
- Shanghai Jiao Tong University（上海交通大学）
- University College London（伦敦大学学院）
- EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本综述提出统一框架，将图拓扑学习与图生成视为图数据共同生成过程的逆问题，综述相关方法并指出跨范式整合机遇，为该领域提供跨学科视角并勾勒未来研究方向。
AI中文摘要：
从数据中学习图结构是信号处理和机器学习领域广泛任务的基础问题。尽管已有大量研究致力于解决该问题，但现有研究主要沿两个平行方向发展：其一，从支撑于图上的观测数据推断单个图的拓扑；其二，从观测到的图实例学习生成分布，从而实现新图的采样。本综述提出一个统一框架，将这些表述关联起来，将其视为图数据共同生成过程的逆问题。我们综述了该框架内的主要方法，强调它们之间的关系、优势与局限性，并指出跨范式整合思想的机遇。通过连接图拓扑学习与图生成，本综述为该领域提供了更广阔的跨学科视角，并勾勒出未来研究的有前景方向。
英文摘要：
Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and machine learning tasks. While significant effort has been made to tackle the problem, existing research has largely evolved along two parallel directions. The first seeks to infer the topology of an individual graph from observations supported on it, whereas the second seeks to learn a generative distribution from observed graph instances, enabling the sampling of new graphs. This review presents a unified framework that connects these formulations by viewing them as inverse problems of a common generation process for graph data. We review the major methodologies within this framework, highlight their relationships, strengths, and limitations, and identify opportunities for integrating ideas across paradigms. By bridging graph topology learning and graph generation, this review provides a broader cross-disciplinary perspective on the field and outlines promising directions for future research.
