arXiv:2607.07232v1 Announce Type: cross Abstract: 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 pr
This research addresses the current limitation of diffusion models in scaling graph generation to large, dense graphs, presenting a novel architectural solution.
Improved graph generation at scale has significant implications for molecular design, drug discovery, and network analysis, enhancing AI's application in complex systems.
The DiPhon framework enables more scalable and robust generative AI for large graphs, potentially accelerating innovation in fields reliant on complex network structures.
- · AI researchers
- · Pharmaceutical companies
- · Materials science
- · Biotechnology sector
- · Traditional graph generation methods
- · Companies reliant on bespoke or inefficient graph design
More efficient and accurate simulation and design of complex structures (e.g., molecules, materials) using AI.
Reduced R&D timelines and costs in fields like drug discovery due to accelerated computational design.
The development of novel materials or therapeutic compounds previously inaccessible due to computational complexity.
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Read at arXiv cs.LG