4 papers
Hierarchical Discrete Flow Matching for Graph Generation
Yoann Boget, Pablo Strasser, Alexandros Kalousis
Denoising-based models, including diffusion and flow matching, have led to substantial advances in graph generation. Despite this progress, such models remain constrained by two fu…
Unrestrained Simplex Denoising for Discrete Data. A Non-Markovian Approach Applied to Graph Generation
Yoann Boget, Alexandros Kalousis
Denoising models such as Diffusion or Flow Matching have recently advanced generative modeling for discrete structures, yet most approaches either operate directly in the discrete…
Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation
Yoann Boget
Discrete Diffusion and Flow Matching models have significantly advanced generative modeling for discrete structures, including graphs. However, the dependencies between intermediat…
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda +1
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre pe…