Image de couverture de livre

GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Yue, Xiao, Qu, Guangzhi, Gan, Lige
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

LiteSemRAG: Lightweight LLM-Free Semantic-Aware Graph Retrieval for Robust RAG
par: Yue, Xiao, et autres
Publié: (2026)

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
par: Wang, Xiaoqi, et autres
Publié: (2022)

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
par: Yu, Zhaoning, et autres
Publié: (2024)

Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
par: Chen, Mengjie, et autres
Publié: (2025)

GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
par: Lu, Hsiao-Ying, et autres
Publié: (2024)

GraphNarrator: Generating Textual Explanations for Graph Neural Networks
par: Pan, Bo, et autres
Publié: (2024)

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
par: Nimase, Ojas, et autres
Publié: (2026)

Graph Neural Network Explanations are Fragile
par: Li, Jiate, et autres
Publié: (2024)

How Interpretable Are Interpretable Graph Neural Networks?
par: Chen, Yongqiang, et autres
Publié: (2024)

MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)
par: Liu, Hengyu, et autres
Publié: (2025)

View-based Explanations for Graph Neural Networks
par: Chen, Tingyang, et autres
Publié: (2024)

The GECo algorithm for Graph Neural Networks Explanation
par: Calderaro, Salvatore, et autres
Publié: (2024)

Graph Neural Network Causal Explanation via Neural Causal Models
par: Behnam, Arman, et autres
Publié: (2024)

RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs
par: Kubo, Ryoji, et autres
Publié: (2025)

PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
par: Shin, Yong-Min, et autres
Publié: (2022)

A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
par: Tremblay, Nicolas, et autres
Publié: (2026)

Quantifying Explanation Quality in Graph Neural Networks using Out-of-Distribution Generalization
par: Zhang, Ding, et autres
Publié: (2026)

SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
par: Cho, Hyeoncheol, et autres
Publié: (2021)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
par: Zhang, Jiaxing, et autres
Publié: (2025)

Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
par: Xie, Fengze, et autres
Publié: (2024)

Faithful Interpretation for Graph Neural Networks
par: Hu, Lijie, et autres
Publié: (2024)

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations
par: Sun, Changsheng, et autres
Publié: (2025)

Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
par: Fan, Shaohua, et autres
Publié: (2021)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
par: Chhablani, Chirag, et autres
Publié: (2024)

Interpreting Temporal Graph Neural Networks with Koopman Theory
par: Guerra, Michele, et autres
Publié: (2024)

Interpretable Graph Neural Networks for Heterogeneous Tabular Data
par: Alkhatib, Amr, et autres
Publié: (2024)

Graph Structure Learning with Interpretable Bayesian Neural Networks
par: Wasserman, Max, et autres
Publié: (2024)

From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
par: Yang, Jie, et autres
Publié: (2025)

Factor Graph-based Interpretable Neural Networks
par: Li, Yicong, et autres
Publié: (2025)

Interpretable Graph Neural Networks for Tabular Data
par: Alkhatib, Amr, et autres
Publié: (2023)

The Interpretable and Effective Graph Neural Additive Networks
par: Bechler-Speicher, Maya, et autres
Publié: (2024)

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
par: Zhang, Jiaxing, et autres
Publié: (2023)

GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
par: Li, Xuyan, et autres
Publié: (2025)

Graphs Unveiled: Graph Neural Networks and Graph Generation
par: Kovács, László, et autres
Publié: (2024)

Design Requirements for Human-Centered Graph Neural Network Explanations
par: Habibi, Pantea, et autres
Publié: (2024)

Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations
par: Naik, Harish G., et autres
Publié: (2024)

Interpreting Graph Inference with Skyline Explanations
par: Qiu, Dazhuo, et autres
Publié: (2025)

GCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level
par: Hu, Jinlong, et autres
Publié: (2026)

Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
par: De Sanctis, Matteo, et autres
Publié: (2026)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
par: Raut, Riddhiman, et autres
Publié: (2025)