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GraphNNK -- Graph Classification and Interpretability

arXiv 2602.00753 ↗First posted 31 Jan 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 31 Jan 2026.

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References · 11

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  4. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  5. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  6. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  7. 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
  8. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 207
  9. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  10. 2020DeepNNK: Explaining deep models and their generalization using polytope interpolationcited by 5
  11. 2021Understanding the Basis of Graph Convolutional Neural Networks via an Intuitive Matched Filtering Approachcited by 3

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