# GraphXAIN: Narratives to Explain Graph Neural Networks

arXiv: https://arxiv.org/abs/2411.02540 · HTML: https://mlsanity.com/paper/2411.02540/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 60 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 30 of 31)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://mlsanity.com/paper/2402.07630/) (2024)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks](https://mlsanity.com/paper/2102.03322/) (2021)
- [Data-to-Text Generation with Content Selection and Planning](https://mlsanity.com/paper/1809.00582/) (2018)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [NICE: An Algorithm for Nearest Instance Counterfactual Explanations](https://mlsanity.com/paper/2104.07411/) (2021)
- [Explaining the Explainers in Graph Neural Networks: a Comparative Study](https://mlsanity.com/paper/2210.15304/) (2022)
- [The Grammar of Interactive Explanatory Model Analysis](https://mlsanity.com/paper/2005.00497/) (2020)
- [Tell Me a Story! Narrative-Driven XAI with Large Language Models](https://mlsanity.com/paper/2309.17057/) (2023)
- [Position: Explain to Question not to Justify](https://mlsanity.com/paper/2402.13914/) (2024)
- [Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem](https://mlsanity.com/paper/2306.13885/) (2023)
- [GraphNarrator: Generating Textual Explanations for Graph Neural Networks](https://mlsanity.com/paper/2410.15268/) (2024)
- [Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables](https://mlsanity.com/paper/2405.15661/) (2024)
- [Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction](https://mlsanity.com/paper/2410.15165/) (2024)
- [How Reliable and Stable are Explanations of XAI Methods?](https://mlsanity.com/paper/2407.03108/) (2024)
- [Natural Language Counterfactual Explanations for Graphs Using Large Language Models](https://mlsanity.com/paper/2410.09295/) (2024)

## Built on by (most cited first, 11 of 11)

- [Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE](https://mlsanity.com/paper/2509.03626/) (2025)
- [An Agentic Approach to Generating XAI-Narratives](https://mlsanity.com/paper/2603.20003/) (2026)
- [Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection](https://mlsanity.com/paper/2506.14933/) (2025)
- [Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation](https://mlsanity.com/paper/2510.03134/) (2025)
- [PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations](https://mlsanity.com/paper/2603.06485/) (2026)
- [Introduction to Graph Neural Networks for Machine Learning Engineers](https://mlsanity.com/paper/2412.19419/) (2024)
- [X-Node: Self-Explanation is All We Need](https://mlsanity.com/paper/2508.10461/) (2025)
- [On the Importance and Evaluation of Narrativity in Natural Language AI Explanations](https://mlsanity.com/paper/2604.18311/) (2026)
- [Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality](https://mlsanity.com/paper/2605.10142/) (2026)
- [CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks](https://mlsanity.com/paper/2606.20747/) (2026)
- [Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification](https://mlsanity.com/paper/2607.17586/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data, yet they pose challenges in interpretability. Existing GNN explanation methods usually yield technical outputs, such as subgraphs and feature importance scores, that are difficult for non-data scientists to understand and thereby violate the purpose of explanations. Motivated by recent Explainable AI (XAI) research, we propose GraphXAIN, a method that generates natural language narratives explaining GNN predictions. GraphXAIN is a model- and explainer-agnostic method that uses Large Language Models (LLMs) to translate explanatory subgraphs and feature importance scores into coherent, story-like explanations of GNN decision-making processes. Evaluations on real-world datasets demonstrate GraphXAIN's ability to improve graph explanations. A survey of machine learning researchers and practitioners reveals that GraphXAIN enhances four explainability dimensions: understandability, satisfaction, convincingness, and suitability for communicating model predictions. When combined with another graph explainer method, GraphXAIN further improves trustworthiness, insightfulness, confidence, and usability. Notably, 95% of participants found GraphXAIN to be a valuable addition to the GNN explanation method. By incorporating natural language narratives, our approach serves both graph practitioners and non-expert users by providing clearer and more effective explanations.