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arXiv · 2602.00753

GraphNNK -- Graph Classification and Interpretability

Zeljko Bolevic·Milos Brajovic·Isidora Stankovic·Ljubisa Stankovic
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Abstract

Graph Neural Networks (GNNs) have become a standard approach for learning from graph-structured data. However, their reliance on parametric classifiers (most often linear softmax layers) limits interpretability and sometimes hinders generalization. Recent work on interpolation-based methods, particularly Non-Negative Kernel regression (NNK), has demonstrated that predictions can be expressed as convex combinations of similar training examples in the embedding space, yielding both theoretical results and interpretable explanations.

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Zeljko Bolevic, Milos Brajovic, Isidora Stankovic, Ljubisa Stankovic. 2026-01-31. GraphNNK -- Graph Classification and Interpretability. https://arxiv.org/abs/2602.00753

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