From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Fuente:
arXiv
Saved in:
| Main Authors: | Baghershahi, Peyman, Fournier, Gregoire, Nyati, Pranav, Medya, Sourav |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unsupervised Prompting for Graph Neural Networks
by: Baghershahi, Peyman, et al.
Published: (2025)
by: Baghershahi, Peyman, et al.
Published: (2025)
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models
by: Zangari, Angelo, et al.
Published: (2026)
by: Zangari, Angelo, et al.
Published: (2026)
Design Requirements for Human-Centered Graph Neural Network Explanations
by: Habibi, Pantea, et al.
Published: (2024)
by: Habibi, Pantea, et al.
Published: (2024)
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
by: Fournier, Gregoire, et al.
Published: (2025)
by: Fournier, Gregoire, et al.
Published: (2025)
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
by: Baghershahi, Peyman, et al.
Published: (2026)
by: Baghershahi, Peyman, et al.
Published: (2026)
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach
by: Chaturvedi, Rochana, et al.
Published: (2025)
by: Chaturvedi, Rochana, et al.
Published: (2025)
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
by: Baghershahi, Peyman, et al.
Published: (2022)
by: Baghershahi, Peyman, et al.
Published: (2022)
Incorporating Heterophily into Graph Neural Networks for Graph Classification
by: Yang, Jiayi, et al.
Published: (2022)
by: Yang, Jiayi, et al.
Published: (2022)
BANGS: Game-Theoretic Node Selection for Graph Self-Training
by: Wang, Fangxin, et al.
Published: (2024)
by: Wang, Fangxin, et al.
Published: (2024)
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
by: Wang, Fangxin, et al.
Published: (2026)
by: Wang, Fangxin, et al.
Published: (2026)
Uncertainty in Graph Neural Networks: A Survey
by: Wang, Fangxin, et al.
Published: (2024)
by: Wang, Fangxin, et al.
Published: (2024)
Explaining Graph Neural Networks for Node Similarity on Graphs
by: Daza, Daniel, et al.
Published: (2024)
by: Daza, Daniel, et al.
Published: (2024)
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
by: Tian, Hao, et al.
Published: (2023)
by: Tian, Hao, et al.
Published: (2023)
Game-theoretic Counterfactual Explanation for Graph Neural Networks
by: Chhablani, Chirag, et al.
Published: (2024)
by: Chhablani, Chirag, et al.
Published: (2024)
GraphXAIN: Narratives to Explain Graph Neural Networks
by: Cedro, Mateusz, et al.
Published: (2024)
by: Cedro, Mateusz, et al.
Published: (2024)
NeuroCUT: A Neural Approach for Robust Graph Partitioning
by: Shah, Rishi, et al.
Published: (2023)
by: Shah, Rishi, et al.
Published: (2023)
Relevant Walk Search for Explaining Graph Neural Networks
by: Xiong, Ping, et al.
Published: (2026)
by: Xiong, Ping, et al.
Published: (2026)
Graph-based Integrated Gradients for Explaining Graph Neural Networks
by: Simpson, Lachlan, et al.
Published: (2025)
by: Simpson, Lachlan, et al.
Published: (2025)
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
by: Chen, Zhuomin, et al.
Published: (2024)
by: Chen, Zhuomin, et al.
Published: (2024)
PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs
by: Shomee, Homaira Huda, et al.
Published: (2025)
by: Shomee, Homaira Huda, et al.
Published: (2025)
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
by: Lu, Shengyao, et al.
Published: (2024)
by: Lu, Shengyao, et al.
Published: (2024)
Preference-driven Knowledge Distillation for Few-shot Node Classification
by: Wei, Xing, et al.
Published: (2025)
by: Wei, Xing, et al.
Published: (2025)
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
by: Guan, Faqian, et al.
Published: (2025)
by: Guan, Faqian, et al.
Published: (2025)
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
by: He, Yuntian, et al.
Published: (2024)
by: He, Yuntian, et al.
Published: (2024)
Adaptive Node Feature Selection For Graph Neural Networks
by: Navarro, Madeline, et al.
Published: (2025)
by: Navarro, Madeline, et al.
Published: (2025)
Towards Invariance to Node Identifiers in Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2025)
by: Bechler-Speicher, Maya, et al.
Published: (2025)
Global-Local Graph Neural Networks for Node-Classification
by: Eliasof, Moshe, et al.
Published: (2024)
by: Eliasof, Moshe, et al.
Published: (2024)
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
by: Pan, Bo, et al.
Published: (2024)
by: Pan, Bo, et al.
Published: (2024)
TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks
by: Liu, Yezi, et al.
Published: (2024)
by: Liu, Yezi, et al.
Published: (2024)
Disambiguated Node Classification with Graph Neural Networks
by: Zhao, Tianxiang, et al.
Published: (2024)
by: Zhao, Tianxiang, et al.
Published: (2024)
Node-level Contrastive Unlearning on Graph Neural Networks
by: Lee, Hong kyu, et al.
Published: (2025)
by: Lee, Hong kyu, et al.
Published: (2025)
Preserving Node-level Privacy in Graph Neural Networks
by: Xiang, Zihang, et al.
Published: (2023)
by: Xiang, Zihang, et al.
Published: (2023)
On the Utilization of Unique Node Identifiers in Graph Neural Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)
by: Bechler-Speicher, Maya, et al.
Published: (2024)
Parallelizing Node-Level Explainability in Graph Neural Networks
by: Llorente, Oscar, et al.
Published: (2026)
by: Llorente, Oscar, et al.
Published: (2026)
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
by: Geng, Chuqin, et al.
Published: (2025)
by: Geng, Chuqin, et al.
Published: (2025)
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
by: Sloneker, Whitney, et al.
Published: (2024)
by: Sloneker, Whitney, et al.
Published: (2024)
Explaining Graph Neural Networks via Structure-aware Interaction Index
by: Bui, Ngoc, et al.
Published: (2024)
by: Bui, Ngoc, et al.
Published: (2024)
Attacks on Node Attributes in Graph Neural Networks
by: Xu, Ying, et al.
Published: (2024)
by: Xu, Ying, et al.
Published: (2024)
Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks
by: Karabulut, Tuğrul Hasan, et al.
Published: (2025)
by: Karabulut, Tuğrul Hasan, et al.
Published: (2025)
Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning
by: Chen, Shiyu, et al.
Published: (2025)
by: Chen, Shiyu, et al.
Published: (2025)
Similar Items
-
Unsupervised Prompting for Graph Neural Networks
by: Baghershahi, Peyman, et al.
Published: (2025) -
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models
by: Zangari, Angelo, et al.
Published: (2026) -
Design Requirements for Human-Centered Graph Neural Network Explanations
by: Habibi, Pantea, et al.
Published: (2024) -
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
by: Fournier, Gregoire, et al.
Published: (2025) -
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
by: Baghershahi, Peyman, et al.
Published: (2026)