Proves universal consistency of GW-k-NN on finite-support metric measure spaces with uniform measure and of fGW-k-NN on node-attributed versions, with competitive empirical performance on graph datasets.
Propagation kernels: Ef- ficient graph kernels from propagated information.Machine Learning, 102:209–245, 2015
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
$k$-Nearest Neighbors in Gromov--Wasserstein Space
Proves universal consistency of GW-k-NN on finite-support metric measure spaces with uniform measure and of fGW-k-NN on node-attributed versions, with competitive empirical performance on graph datasets.