mlsanityData to 14 Oct 2026

Towards Automated Evaluation of Explanations in Graph Neural Networks

arXiv 2106.11864 ↗First posted 22 Jun 2021cs.AI cs.LGcited by 2

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Abstract (arXiv)

Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem. In particular, we do not have well developed methods for automatically evaluating explanations, in ways that are closer to how users consume those explanations. Based on recent application trends and our own experiences in real world problems, we propose automatic evaluation approaches for GNN Explanations.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

14 of 19 references.

References · 14

  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. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  4. 2016Variational Graph Auto-Encoderscited by 1,382
  5. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
  6. 2020Parameterized Explainer for Graph Neural Networkcited by 426
  7. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  8. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
  9. 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
  10. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
  11. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 153
  12. 2021Graph Neural Network to Dilute Outliers for Refactoring Monolith Applicationcited by 9
2 more
  1. 2020Link Prediction using Graph Neural Networks for Master Data Managementcited by 8
  2. 2020Explainable Link Prediction for Privacy-Preserving Contact Tracingcited by 4

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Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.

  1. 2021Reimagining GNN Explanations with ideas from Tabular Datacited by 1
  2. 2022xEM: Explainable Entity Matching in Customer 360cited by 0