From topology learning to graph generation: A unifying perspective
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 frame...
| Authors | Xiaowen Dong, Hoi-To Wai, Siheng Chen et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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