Message-passing selection: Towards interpretable GNNs for graph classification
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Abstract (arXiv)
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines. Unlike the most existing explanation methods, MSInterpreter provides a Message-passing Selection scheme(MSScheme) to select the critical paths for GNNs' message aggregations, which aims at reaching the self-explaination instead of post-hoc explanations. In detail, the elaborate MSScheme is designed to calculate weight factors of message aggregation paths by considering the vanilla structure and node embedding components, where the structure base aims at weight factors among node-induced substructures; on the other hand, the node embedding base focuses on weight factors via node embeddings obtained by one-layer GNN.Finally, we demonstrate the effectiveness of our approach on graph classification benchmarks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 34 references that are in the map. First posted on arXiv 3 Jun 2023.
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28 of 34 references.
References · 28
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
- 2020Parameterized Explainer for Graph Neural Networkcited by 426
- 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
- 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 281
- 2020XGNN: Towards Model-Level Explanations of Graph Neural Networkscited by 211
- 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 211
- 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
- 2020Interpreting Graph Neural Networks for NLP With Differentiable Edge Maskingcited by 153
- 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
- 2022Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoningcited by 79
- 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
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- 2021Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networkscited by 15
- 2023Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalizationcited by 12
- 2022MotifExplainer: a Motif-based Graph Neural Network Explainercited by 11
- 2022Interaction Pattern Disentangling for Multi-Agent Reinforcement Learningcited by 8
- 2021Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivitycited by 6
- 2021Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGANcited by 4
- 2022Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power Systemcited by 3
- 2019More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework based Kernel Matrix Representationcited by 2
- 2023Schema Inference for Interpretable Image Classificationcited by 1
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