most citedRandom Walk Diffusion for Efficient Large-Scale Graph Generation

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cs.LG2024★ 2 cited

Random Walk Diffusion for Efficient Large-Scale Graph Generation

Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale +2

Graph generation addresses the problem of generating new graphs that have a data distribution similar to real-world graphs. While previous diffusion-based graph generation methods…

cs.LG2023

Mixed Models with Multiple Instance Learning

Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak +2

Predicting patient features from single-cell data can help identify cellular states implicated in health and disease. Linear models and average cell type expressions are typically…