Related papers: From topology learning to graph generation: A unifying perspective

From topology learning to graph generation: A unifying perspective

URL: http://arxiv.org/abs/2609.02286v1
Date: Wed, 02 Sep 2026 08:36:36 GMT
Title: From topology learning to graph generation: A unifying perspective
Abstract summary: Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and machine learning tasks.<n>Existing research has largely evolved along two parallel directions.<n>This review presents a unified framework that connects these formulations by viewing them as inverse problems of a common generation process for graph data.
Score: 61.18730621087076
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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