Semi-relaxed Gromov-Wasserstein divergence with applications on graphs. (arXiv:2110.02753v2 [cs.LG] UPDATED)

Comparing structured objects such as graphs is a fundamental operation
involved in many learning tasks. To this end, the Gromov-Wasserstein (GW)
distance, based on Optimal Transport (OT), has proven to be successful in
handling the specific nature of the associated objects. More specifically,
through the nodes connectivity relations, GW operates on graphs, seen as
probability measures over specific spaces. At the core of OT is the idea of
conservation of mass, which imposes a coupling between all the nodes from …

applications arxiv divergence graphs