Generative Modeling on Metric Graphs via Neural Optimal Transport
Researchers have developed a novel deep generative modeling framework designed for probability distributions on metric graphs. This method embeds graphs into ambient spaces to solve optimal transport problems, projecting generated samples back onto the graph. The approach demonstrates improved scalability and performance compared to heuristic baselines, particularly on real-world urban mobility data. AI