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Gromov-Monge Flow Matching for Equivariant Graph Generation
Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in generative models. In flow matching, however, symmetry may also enter the source--target coupling: once graph pairs are compared up to node relabeling, the natural Wasserstein geometry is that of the graph quotient space. The Euclidean quotient metric of this space coincides with the Gromov--Monge distance, obtained by optimally relabeling the nodes. We develop this perspective theoretically, showing that quotient couplings can be lifted to aligned representatives without additional cos
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T11:01:03.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.