Image de couverture de livre

Global Graph Counterfactual Explanation: A Subgraph Mapping Approach

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	He, Yinhan, Zheng, Wendy, Zhu, Yaochen, Ma, Jing, Mishra, Saumitra, Raman, Natraj, Liu, Ninghao, Li, Jundong
Format:	Preprint
Publié:	2024
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

Causal Inference with Latent Variables: Recent Advances and Future Prospectives
par: Zhu, Yaochen, et autres
Publié: (2024)

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
par: He, Yinhan, et autres
Publié: (2024)

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning
par: He, Yinhan, et autres
Publié: (2026)

Edge Prompt Tuning for Graph Neural Networks
par: Fu, Xingbo, et autres
Publié: (2025)

MolEdit: Knowledge Editing for Multimodal Molecule Language Models
par: Lei, Zhenyu, et autres
Publié: (2025)

Spectral Greedy Coresets for Graph Neural Networks
par: Ding, Mucong, et autres
Publié: (2024)

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
par: Hamman, Faisal, et autres
Publié: (2023)

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
par: Dong, Yushun, et autres
Publié: (2024)

PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling
par: Venkatesh, Kavana, et autres
Publié: (2026)

Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
par: Wang, Song, et autres
Publié: (2025)

Scalable Representation Learning for Multimodal Tabular Transactions
par: Raman, Natraj, et autres
Publié: (2024)

Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
par: Liu, Haochen, et autres
Publié: (2024)

Energy-Based Models for Predicting Mutational Effects on Proteins
par: Soga, Patrick, et autres
Publié: (2025)

Saliency-Aware Multi-Route Thinking: Revisiting Vision-Language Reasoning
par: Shi, Mingjia, et autres
Publié: (2026)

Interpreting Language Reward Models via Contrastive Explanations
par: Jiang, Junqi, et autres
Publié: (2024)

Graph Diffusion Counterfactual Explanation
par: Bechtoldt, David, et autres
Publié: (2025)

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

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
par: Fu, Xingbo, et autres
Publié: (2024)

Info-CELS: Informative Saliency Map Guided Counterfactual Explanation
par: Li, Peiyu, et autres
Publié: (2024)

Safety in Graph Machine Learning: Threats and Safeguards
par: Wang, Song, et autres
Publié: (2024)

Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
par: Cao, Tianyu, et autres
Publié: (2024)

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

Robust Stochastic Graph Generator for Counterfactual Explanations
par: Prado-Romero, Mario Alfonso, et autres
Publié: (2023)

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
par: Fu, Xingbo, et autres
Publié: (2025)

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

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

Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
par: Wu, Xuansheng, et autres
Publié: (2024)

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
par: Lu, Shengyao, et autres
Publié: (2024)

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

Graph Edits for Counterfactual Explanations: A comparative study
par: Dimitriou, Angeliki, et autres
Publié: (2024)

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
par: Wang, Danqing, et autres
Publié: (2024)

Tabular Diffusion Counterfactual Explanations
par: Zhang, Wei, et autres
Publié: (2025)

RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation
par: Qu, Jingxiang, et autres
Publié: (2025)

Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
par: Tang, Wenzhuo, et autres
Publié: (2024)

Motif-guided Time Series Counterfactual Explanations
par: Li, Peiyu, et autres
Publié: (2022)

Instance-Aware Graph Prompt Learning
par: Li, Jiazheng, et autres
Publié: (2024)

Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
par: Liu, Bowen, et autres
Publié: (2024)

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
par: Kostrzewa, Marcin, et autres
Publié: (2026)

Watermarking Counterfactual Explanations
par: Guo, Hangzhi, et autres
Publié: (2024)

ACE: Adapting sampling for Counterfactual Explanations
par: Guerrero, Margarita A., et autres
Publié: (2025)