GNN-parametrized continuous normalizing flows for graph signals are permutation equivariant and satisfy Wasserstein stability bounds under relative graph perturbations, motivating a Lipschitz-regularized training strategy.
Flow matching for generative modeling,
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Stability of Flow Models for Graph Signals
GNN-parametrized continuous normalizing flows for graph signals are permutation equivariant and satisfy Wasserstein stability bounds under relative graph perturbations, motivating a Lipschitz-regularized training strategy.