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.
Bioinformatics21 (06 2005)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
AIM is a new evaluation framework for explainability in GNNs that combines accuracy, instance-level, and model-level measures, applied to graph kernel networks to create an improved model xGKN.
An explanation-based detector using seven novel metrics derived from GNN explanations identifies backdoored graphs with high performance on benchmark datasets against multiple attack models.
citing papers explorer
No citing papers match the current filters.