GDIFFTABLE: GRAPH DIFFUSION FOR TABULAR DATA SYNTHESIS

Tabular synthesis requires preserving dependencies among heterogeneous columns, yet diffusion progressively corrupts the row-level evidence for those dependencies. We introduce GDiffTable, a mixed-type tabular diffusion model that represents columns as graph nodes and jointly learns a dataset-level weighted adjacency with a time-conditioned message-passing denoiser. The adjacency is shared across diffusion timesteps to provide a stable scaffold for cross-column coupling, while the exchanged messages adapt to the current noisy state and time embedding. We further introduce correctable graph initialization: an offline schema-derived relation prior determines only the initial adjacency, and all off-diagonal weights remain trainable under the denoising objective. Consequently, initially absent relations can emerge, selected relations can be weakened, and the formulation remains compatible with non-LLM initializers. Across seven benchmarks, GDiffTable reports the highest aggregate point estimates for Shape (98.74%) , together with the smallest average downstream gap to real data (4.50%) among the evaluated synthesizers. Adult and News comparisons indicate that cross-column communication improves over node-wise denoising, learned non-uniform routing improves over fixed uniform mixing, and refinement improves over a frozen prior. The full and data-only graph variants attain similar final quality; in the reported runs, LLM initialization reduces their early-stopping times by 38.5% and 59.3%. These results support graph-structured diffusion denoising while positioning the LLM prior as an optional optimization warm start.