Awesome Explainable Graph Learning

Survey, Evaluation


  • [ICDM 2021] Multi-objective Explanations of GNN Predictions [paper]
  • [CIKM 2021] Towards Self-Explainable Graph Neural Network [paper]
  • [ECML PKDD 2021] GraphSVX: Shapley Value Explanations for Graph Neural Networks [paper]
  • [NeSy 21] A New Concept for Explaining Graph Neural Networks [paper]
  • [ICML workstop 2020] Contrastive Graph Neural Network Explanation [paper]
  • [Arxiv 2020] Graph Neural Networks Including Sparse Interpretability [paper]
  • [OpenReview 20] A Framework For Differentiable Discovery Of Graph Algorithms [paper]
  • [OpenReview 20] Causal Screening to Interpret Graph Neural Networks [paper]
  • [Arxiv 20] Understanding Graph Neural Networks from Graph Signal Denoising Perspectives [paper]
  • [Arxiv 20] Understanding the Message Passing in Graph Neural Networks via Power Iteration [paper]
  • [IJCNN 20] GCN-LRP explanation: exploring latent attention of graph convolutional networks ] [paper]


  • [BioRxiv 22] GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks [paper]
  • [PAKDD 21] SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction [paper]
  • [CVPR 2021] Quantifying Explainers of Graph Neural Networks in Computational Pathology .[paper]
  • [ICML 2021] Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity [paper]
  • [ICML workshop 21] GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks [paper]
  • [ICML workshop 21] BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis [paper]
  • [ICML workshop 21] Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction [paper]
  • [KDD 2021] Counterfactual Graphs for Explainable Classification of Brain Networks [paper]
  • [IJCNN 21] MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks [paper]