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Sparse training of discrete diffusion models for graph generation
Generative graph models struggle to scale due to the need to predict the existence or type of
edges between all node pairs. To address the resulting quadratic complexity, existing …
edges between all node pairs. To address the resulting quadratic complexity, existing …
Graph Generation with -trees
Generating graphs from a target distribution is a significant challenge across many domains,
including drug discovery and social network analysis. In this work, we introduce a novel …
including drug discovery and social network analysis. In this work, we introduce a novel …
A simple and scalable representation for graph generation
Recently, there has been a surge of interest in employing neural networks for graph
generation, a fundamental statistical learning problem with critical applications like molecule …
generation, a fundamental statistical learning problem with critical applications like molecule …
Sparse Training of Discrete Diffusion Models for Graph Generation
QIN Yiming, C Vignac, P Frossard - openreview.net
Generative models for graphs often encounter scalability challenges due to the inherent
need to predict interactions for every node pair. Despite the sparsity often exhibited by real …
need to predict interactions for every node pair. Despite the sparsity often exhibited by real …