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)
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
path_boost packages PathBoost, an interpretable path-based gradient booster for graphs that is competitive with GINE and WL+SVR on six molecular regression datasets while exposing which labeled paths drive predictions.
citing papers explorer
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$k$-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.
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AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
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.
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Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
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.
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path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
path_boost packages PathBoost, an interpretable path-based gradient booster for graphs that is competitive with GINE and WL+SVR on six molecular regression datasets while exposing which labeled paths drive predictions.