# A Survey on Explainability of Graph Neural Networks

arXiv: https://arxiv.org/abs/2306.01958 · HTML: https://mlsanity.com/paper/2306.01958/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 83 of its 128 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 30 of 83)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [GFlowNet Foundations](https://mlsanity.com/paper/2111.09266/) (2021)
- [On Explainability of Graph Neural Networks via Subgraph Explorations](https://mlsanity.com/paper/2102.05152/) (2021)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020)
- [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020)
- [GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2001.06216/) (2020)
- [Discovering Invariant Rationales for Graph Neural Networks](https://mlsanity.com/paper/2201.12872/) (2022)
- [Compositional Explanations of Neurons](https://mlsanity.com/paper/2006.14032/) (2020)
- [Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism](https://mlsanity.com/paper/2201.12987/) (2022)
- [Understanding Attention and Generalization in Graph Neural Networks](https://mlsanity.com/paper/1905.02850/) (2019)
- [Explainability Techniques for Graph Convolutional Networks](https://mlsanity.com/paper/1905.13686/) (2019)
- [Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking](https://mlsanity.com/paper/2010.00577/) (2020)
- [Higher-Order Explanations of Graph Neural Networks via Relevant Walks](https://mlsanity.com/paper/2006.03589/) (2020)
- [Generative Causal Explanations for Graph Neural Networks](https://mlsanity.com/paper/2104.06643/) (2021)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)

## Built on by (most cited first, 29 of 29)

- [Uncertainty in Graph Neural Networks: A Survey](https://mlsanity.com/paper/2403.07185/) (2024)
- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs](https://mlsanity.com/paper/2406.15156/) (2024)
- [GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking](https://mlsanity.com/paper/2310.01794/) (2023)
- [Explaining Graph Neural Networks via Structure-aware Interaction Index](https://mlsanity.com/paper/2405.14352/) (2024)
- [Recent Advances in Malware Detection: Graph Learning and Explainability](https://mlsanity.com/paper/2502.10556/) (2025)
- [How Faithful are Self-Explainable GNNs?](https://mlsanity.com/paper/2308.15096/) (2023)
- [Explainable Graph Neural Networks Under Fire](https://mlsanity.com/paper/2406.06417/) (2024)
- [Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations](https://mlsanity.com/paper/2403.07849/) (2024)
- [xAI-Drop: Don't Use What You Cannot Explain](https://mlsanity.com/paper/2407.20067/) (2024)
- [CausalAffect: Causal Discovery for Facial Affective Understanding](https://mlsanity.com/paper/2512.00456/) (2025)
- [Interpreting Temporal Graph Neural Networks with Koopman Theory](https://mlsanity.com/paper/2410.13469/) (2024)
- [Superposition in Graph Neural Networks](https://mlsanity.com/paper/2509.00928/) (2025)
- [Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning](https://mlsanity.com/paper/2407.00849/) (2024)
- [Disentangled and Self-Explainable Node Representation Learning](https://mlsanity.com/paper/2410.21043/) (2024)
- [MotifGPL: Motif-Enhanced Graph Prototype Learning for Deciphering Urban Social Segregation](https://mlsanity.com/paper/2412.18464/) (2024)
- [From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP](https://mlsanity.com/paper/2504.02064/) (2025)
- [Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies](https://mlsanity.com/paper/2508.11513/) (2025)
- [Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction](https://mlsanity.com/paper/2408.17129/) (2024)
- [NAEx: A Plug-and-Play Framework for Explaining Network Alignment](https://mlsanity.com/paper/2508.04731/) (2025)
- [GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification](https://mlsanity.com/paper/2510.00374/) (2025)
- [ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining](https://mlsanity.com/paper/2511.22866/) (2025)
- [ATLAS: Adaptive Topology-based Learning at Scale for Homophilic and Heterophilic Graphs](https://mlsanity.com/paper/2512.14908/) (2025)
- [BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop](https://mlsanity.com/paper/2603.01941/) (2026)
- [Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning](https://mlsanity.com/paper/2604.11986/) (2026)
- [LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions](https://mlsanity.com/paper/2605.19915/) (2026)
- [Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models](https://mlsanity.com/paper/2608.17715/) (2026)
- [Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution](https://mlsanity.com/paper/2608.27574/) (2026)
- [HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases](https://mlsanity.com/paper/2609.33972/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems. However, combining feature information and combinatorial graph structures has led to complex non-linear GNN models. Consequently, this has increased the challenges of understanding the workings of GNNs and the underlying reasons behind their predictions. To address this, numerous explainability methods have been proposed to shed light on the inner mechanism of the GNNs. Explainable GNNs improve their security and enhance trust in their recommendations. This survey aims to provide a comprehensive overview of the existing explainability techniques for GNNs. We create a novel taxonomy and hierarchy to categorize these methods based on their objective and methodology. We also discuss the strengths, limitations, and application scenarios of each category. Furthermore, we highlight the key evaluation metrics and datasets commonly used to assess the explainability of GNNs. This survey aims to assist researchers and practitioners in understanding the existing landscape of explainability methods, identifying gaps, and fostering further advancements in interpretable graph-based machine learning.