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This review establishes a unified framework that connects the inference of graph topology from observations with the learning of generative distributions for graph instances, treating both as inverse problems of a shared generation process. By systematically analyzing existing methodologies, the authors highlight the interrelationships, strengths, and limitations of these approaches, paving the way for cross-pollination of ideas. The findings suggest that integrating these two paradigms could significantly advance the field of graph learning and generation.
Bridging the gap between graph topology inference and generative modeling could unlock new avenues for innovation in graph learning.
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.
