mlsanityData to 14 Oct 2026

Reimagining GNN Explanations with ideas from Tabular Data

arXiv 2106.12665 ↗First posted 23 Jun 2021cs.LG cs.AIcited by 1

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Abstract (arXiv)

Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained on tabular data. Using a task that straddles both graphs and tabular data, namely Entity Matching, we comment on key aspects of explainability that are missing in GNN model explanations.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 15 references that are in the map. First posted on arXiv 23 Jun 2021.

Builds on

13 of 15 references.

References · 13

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  4. 2019TabNet: Attentive Interpretable Tabular Learningcited by 648
  5. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  6. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  7. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
  8. 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 153
  9. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 153
  10. 2020Link Prediction using Graph Neural Networks for Master Data Managementcited by 8
  11. 2020Explainable Link Prediction for Privacy-Preserving Contact Tracingcited by 4
  12. 2020Data Augmentation for Personal Knowledge Base Populationcited by 3
1 more
  1. 2021Towards Automated Evaluation of Explanations in Graph Neural Networkscited by 2

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Citing papers per year: 2022: 1.

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  1. 2022xEM: Explainable Entity Matching in Customer 360cited by 0