mlsanityData to 7 Oct 2026

Evaluating Link Prediction Explanations for Graph Neural Networks

arXiv 2308.01682 ↗First posted 3 Aug 2023cs.LG cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has received little attention. In this paper, we provide quantitative metrics to assess the quality of link prediction explanations, with or without ground-truth. State-of-the-art explainability methods for Graph Neural Networks are evaluated using these metrics. We discuss how underlying assumptions and technical details specific to the link prediction task, such as the choice of distance between node embeddings, can influence the quality of the explanations.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 51 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

31 of 51 references.

References · 31

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  4. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  5. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  6. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  7. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  8. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  9. 2016Variational Graph Auto-Encoderscited by 1,373
  10. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  11. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  12. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
19 more
  1. 2018Link Prediction Based on Graph Neural Networkscited by 829
  2. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  3. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  4. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  5. 2019Hyperbolic Graph Convolutional Neural Networkscited by 400
  6. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  7. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  8. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  9. 2018To Trust Or Not To Trust A Classifiercited by 205
  10. 2021Identity-aware Graph Neural Networkscited by 197
  11. 2019Explainability Techniques for Graph Convolutional Networkscited by 160
  12. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  13. 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 152
  14. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  15. 2022Evaluating Explainability for Graph Neural Networkscited by 85
  16. 2021Link Prediction with Persistent Homology: An Interactive Viewcited by 37
  17. 2021Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methodscited by 36
  18. 2021Variational Graph Normalized Auto-Encoderscited by 29
  19. 2022Task-Agnostic Graph Explanationscited by 20

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networkscited by 1
  2. 2026Parallelizing Node-Level Explainability in Graph Neural Networkscited by 0