A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks

This publication has 31 references indexed in Scilit:

• Counterfactual Learning on Graphs: A Survey
  Machine Intelligence Research, 2025
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
  Machine Intelligence Research, 2024
• L2XGNN: learning to explain graph neural networks
  Machine Learning, 2024
• Knowledge Enhanced Graph Neural Networks
  2023
• A comprehensive survey of link prediction methods
  The Journal of Supercomputing, 2023
• Evaluating explainability for graph neural networks
  Scientific Data, 2023
• Link Prediction on Complex Networks: An Experimental Survey
  Data Science and Engineering, 2022
• MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks
  2021
• Explanation in artificial intelligence: Insights from the social sciences
  Artificial Intelligence, 2019
• Probable Inference, the Law of Succession, and Statistical Inference
  Journal of the American Statistical Association, 1927