DiPhon: Diffusion on Graphons for Scalable Graph Generation
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem. We approach this question in the dense-graph setting through the lens of graphons, the size-agnostic limit objects of dense graph sequences, to study how structural graph statistics behave across node-size scales. This perspective leads to DiPhon, a diffusion framework for size-scalable graph generation. Specifically, we formulate a continuous diffusion process on the graphon space via a Jacobi stochastic differential equation (SDE), and propose DiPhon, a discretized graph-level process that mimics t...
| Authors | Sergio Rozada, Yiming Qin, Manuel Madeira et al. |
|---|---|
| Primary category | stat.ML |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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