Cover Image

Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Teufel, Jonas, Torresi, Luca, Friederich, Pascal
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MEGAN: Multi-Explanation Graph Attention Network
by: Teufel, Jonas, et al.
Published: (2022)

Global Concept Explanations for Graphs by Contrastive Learning
by: Teufel, Jonas, et al.
Published: (2024)

Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
by: Teufel, Jonas, et al.
Published: (2025)

Hyper-Dimensional Fingerprints as Molecular Representations
by: Teufel, Jonas, et al.
Published: (2026)

Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs
by: Torresi, Luca, et al.
Published: (2025)

Symmetry-Aware Bayesian Flow Networks for Crystal Generation
by: Ruple, Laura, et al.
Published: (2025)

Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
by: Deng, Huiqi, et al.
Published: (2025)

Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
by: Schopmans, Henrik, et al.
Published: (2024)

Latent Communication in Artificial Neural Networks
by: Moschella, Luca
Published: (2024)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
by: Zhang, Jiaxing, et al.
Published: (2025)

The GECo algorithm for Graph Neural Networks Explanation
by: Calderaro, Salvatore, et al.
Published: (2024)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
by: Chhablani, Chirag, et al.
Published: (2024)

Graph Neural Network Causal Explanation via Neural Causal Models
by: Behnam, Arman, et al.
Published: (2024)

How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
by: Ma, Bin, et al.
Published: (2025)

ALMANACS: A Simulatability Benchmark for Language Model Explainability
by: Mills, Edmund, et al.
Published: (2023)

Logical Distillation of Graph Neural Networks
by: Pluska, Alexander, et al.
Published: (2024)

GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
by: Li, Xuyan, et al.
Published: (2025)

Intrinsic Lorentz Neural Network
by: Shi, Xianglong, et al.
Published: (2026)

A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
by: Wang, Yucheng, et al.
Published: (2024)

Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
by: Downer, Jane, et al.
Published: (2024)

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
by: Wang, Xiaoqi, et al.
Published: (2022)

GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks
by: Sanderson, Jacob, et al.
Published: (2026)

Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
by: Beddar-Wiesing, Silvia, et al.
Published: (2022)

Graph Neural Networks based Log Anomaly Detection and Explanation
by: Li, Zhong, et al.
Published: (2023)

Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
by: Qharabagh, Muhammad Fetrat, et al.
Published: (2026)

Regularizing Explanations in Bayesian Convolutional Neural Networks
by: Bekkemoen, Yanzhe, et al.
Published: (2021)

Pathwise Explanation of ReLU Neural Networks
by: Lim, Seongwoo, et al.
Published: (2025)

Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest
by: Ceschin, Matteo, et al.
Published: (2025)

Learning Quantifiable Visual Explanations Without Ground-Truth
by: Singh, Amritpal, et al.
Published: (2026)

Unifying Attribution-Based Explanations Using Functional Decomposition
by: Gevaert, Arne, et al.
Published: (2024)

Physics-Informed Graph Neural Networks for Water Distribution Systems
by: Ashraf, Inaam, et al.
Published: (2024)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
by: Mulye, Mihir, et al.
Published: (2024)

FAME: Formal Abstract Minimal Explanation for Neural Networks
by: Boumazouza, Ryma, et al.
Published: (2026)

A Novel Multimodal RUL Framework for Remaining Useful Life Estimation with Layer-wise Explanations
by: Razzaq, Waleed, et al.
Published: (2025)

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
by: Yu, Zhaoning, et al.
Published: (2024)

TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks
by: Wang, Ta-Yang, et al.
Published: (2026)

Obtaining Example-Based Explanations from Deep Neural Networks
by: Dong, Genghua, et al.
Published: (2025)

A Model of Causal Explanation on Neural Networks for Tabular Data
by: Isozaki, Takashi, et al.
Published: (2025)

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
by: Zhang, Jiaxing, et al.
Published: (2023)

CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks
by: Liu, Tao, et al.
Published: (2025)