AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Graph diffusion models are competitive approaches for generating complex graph-structured data. However, when such distributions have strict structural constraints, diffusion models may fail to capture them exactly, resulting in the generation of invalid graphs. In this paper, we propose a method for grounding graph di...
This work proposes a unified autoregressive diffusion-based graph generative model that jointly learns structure and features, constructing complete 3DSGs bottom-up from observed vertical planes across arbitrary hierarchy depths, and proposes an adaptation of the Fused Gromov--Wasserstein distance for principled graph-...
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