Problem
Research questions and friction points this paper is trying to address.
graph structure learning
topology inference
graph generation
From topology learning to graph generation: A unifying perspective
🤖 AI Summary
该文提出一个统一框架，将图拓扑学习和图生成视为共同生成过程的逆问题，以解决从数据中学习图结构的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
unified framework
graph topology learning
graph generation
inverse problems
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