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Towards Prototype-Based Self-Explainable Graph Neural Network

https://doi.org/10.1145/3689647
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1016/0377-0427(87)90125-7openalex-snapshot
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
inferredcites →https://doi.org/10.1016/j.neucom.2021.04.039openalex-snapshot
Molecular generative Graph Neural Networks for Drug Discovery
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1038/s42256-019-0048-xopenalex-snapshot
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
inferredcites →https://doi.org/10.1039/c7sc02664aopenalex-snapshot
MoleculeNet: a benchmark for molecular machine learning
inferredcites →https://doi.org/10.1109/cvpr.2019.01103openalex-snapshot
Explainability Methods for Graph Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/iccv.2019.00936openalex-snapshot
DeepGCNs: Can GCNs Go As Deep As CNNs?
inferredcites →https://doi.org/10.1109/tkde.2022.3187455openalex-snapshot
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2022.3204236openalex-snapshot
Explainability in Graph Neural Networks: A Taxonomic Survey
inferredcites →https://doi.org/10.1109/tsp.2018.2879624openalex-snapshot
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/3219819.3219890openalex-snapshot
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
inferredcites →https://doi.org/10.1145/3219819.3220099openalex-snapshot
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
inferredcites →https://doi.org/10.1145/3292500.3330836openalex-snapshot
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
inferredcites →https://doi.org/10.1145/3292500.3330925openalex-snapshot
Cluster-GCN
inferredcites →https://doi.org/10.1145/3292500.3330935openalex-snapshot
dEFEND
inferredcites →https://doi.org/10.1145/3306618.3314273openalex-snapshot
TED
inferredcites →https://doi.org/10.1145/3340531.3411977openalex-snapshot
Semi-Supervised Graph-to-Graph Translation
inferredcites →https://doi.org/10.1145/3375627openalex-snapshot
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
inferredcites →https://doi.org/10.1145/3394486.3403085openalex-snapshot
XGNN: Towards Model-Level Explanations of Graph Neural Networks
inferredcites →https://doi.org/10.1145/3394486.3403168openalex-snapshot
GCC
inferredcites →https://doi.org/10.1145/3459637.3482306openalex-snapshot
Towards Self-Explainable Graph Neural Network
inferredcites →https://doi.org/10.1186/s13321-020-00479-8openalex-snapshot
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
inferredcites →https://doi.org/10.1609/aaai.v32i1.11771openalex-snapshot
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions
inferredcites →https://doi.org/10.1609/aaai.v33i01.33015717openalex-snapshot
Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods
inferredcites →https://doi.org/10.4230/lipics.giscience.2025.9openalex-snapshot
Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach
inferredcites →https://doi.org/10.48550/arxiv.2010.13902openalex-snapshot
Graph Contrastive Learning with Augmentations
inferredcites →https://doi.org/10.48550/arxiv.2011.04573openalex-snapshot
Parameterized Explainer for Graph Neural Network

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/wcms.70049openalex-snapshot
Explainable Artificial Intelligence in Drug Discovery: Bridging Predictive Power and Mechanistic Insight
inferred← citeshttps://doi.org/10.1007/978-3-031-63787-2_10openalex-snapshot
Global Concept Explanations for Graphs by Contrastive Learning
inferred← citeshttps://doi.org/10.1007/978-3-032-37664-0_38openalex-snapshot
Optimal Transport Prototypes and Symbolic Logic Combination for Self-explainable Graph Neural Networks
inferred← citeshttps://doi.org/10.1007/s11633-024-1519-zopenalex-snapshot
Counterfactual Learning on Graphs: A Survey
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102221openalex-snapshot
GAGE: A graph-based and prototype-guided framework for fine-grained visual ginseng age estimation
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2025.109528openalex-snapshot
Explainable Neural Network meets Graph Neural Network: Recent advances in process fault detection and diagnosis
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.131466openalex-snapshot
Community-strength-based fairness in GNN explanations for heterogeneous link prediction
inferred← citeshttps://doi.org/10.1109/asens69964.2026.11605262openalex-snapshot
Research on Big Data Mining Methods Based on Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/icde55515.2023.00056openalex-snapshot
Jointly Attacking Graph Neural Network and its Explanations
inferred← citeshttps://doi.org/10.1109/icde60146.2024.00229openalex-snapshot
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
inferred← citeshttps://doi.org/10.1145/3639295openalex-snapshot
View-based Explanations for Graph Neural Networks
inferred← citeshttps://doi.org/10.1145/3711896.3737089openalex-snapshot
PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep IP Protection
inferred← citeshttps://doi.org/10.1145/3712184openalex-snapshot
Introduction for the Special Issue on Trustworthy Artificial Intelligence
inferred← citeshttps://doi.org/10.1145/3794856openalex-snapshot
Counterfactual Explanations for Cautious Random Forests
inferred← citeshttps://doi.org/10.56294/shp2025227openalex-snapshot
Understanding the working mechanism of neural networks
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