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Found 3 papers, 1 papers with code
The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs
This new interpretation connects the WL distance to the literature on distances for stochastic processes, which also makes the interpretation of the distance more accessible and intuitive.
Learning Ultrametric Trees for Optimal Transport Regression
For measures supported in discrete metric spaces, finding the optimal transport distance has cubic time complexity in the size of the space.
Weisfeiler-Lehman meets Gromov-Wasserstein
The WL distance is polynomial time computable and is also compatible with the WL test in the sense that the former is positive if and only if the WL test can distinguish the two involved graphs.
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