# MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation

arXiv: https://arxiv.org/abs/2307.07832 · HTML: https://mlsanity.com/paper/2307.07832/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 58 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [On Explainability of Graph Neural Networks via Subgraph Explorations](https://mlsanity.com/paper/2102.05152/) (2021)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism](https://mlsanity.com/paper/2201.12987/) (2022)
- [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020)
- [Explainability Techniques for Graph Convolutional Networks](https://mlsanity.com/paper/1905.13686/) (2019)
- [G-Mixup: Graph Data Augmentation for Graph Classification](https://mlsanity.com/paper/2202.07179/) (2022)
- [Explaining Image Classifiers by Counterfactual Generation](https://mlsanity.com/paper/1807.08024/) (2018)
- [Graph Information Bottleneck for Subgraph Recognition](https://mlsanity.com/paper/2010.05563/) (2020)
- [Molecular geometry prediction using a deep generative graph neural network](https://mlsanity.com/paper/1904.00314/) (2019)
- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Towards Self-Explainable Graph Neural Network](https://mlsanity.com/paper/2108.12055/) (2021)
- [Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels](https://mlsanity.com/paper/2201.00232/) (2022)
- [C-Mixup: Improving Generalization in Regression](https://mlsanity.com/paper/2210.05775/) (2022)
- [GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction](https://mlsanity.com/paper/2106.11133/) (2021)
- [Explaining the Explainers in Graph Neural Networks: a Comparative Study](https://mlsanity.com/paper/2210.15304/) (2022)
- [Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering](https://mlsanity.com/paper/1808.04126/) (2018)

## Built on by (most cited first, 18 of 18)

- [Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks](https://mlsanity.com/paper/2310.01820/) (2023)
- [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://mlsanity.com/paper/2402.02464/) (2024)
- [Factorized Explainer for Graph Neural Networks](https://mlsanity.com/paper/2312.05596/) (2023)
- [LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation](https://mlsanity.com/paper/2407.15351/) (2024)
- [Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation](https://mlsanity.com/paper/2402.08845/) (2024)
- [Powerformer: A Section-adaptive Transformer for Power Flow Adjustment](https://mlsanity.com/paper/2401.02771/) (2024)
- [SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction](https://mlsanity.com/paper/2510.00080/) (2025)
- [FROG: Fair Removal on Graphs](https://mlsanity.com/paper/2503.18197/) (2025)
- [Spectral Manifold Regularization for Stable and Modular Routing in Deep MoE Architectures](https://mlsanity.com/paper/2601.03889/) (2026)
- [Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks](https://mlsanity.com/paper/2402.02036/) (2024)
- [RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation](https://mlsanity.com/paper/2505.02247/) (2025)
- [Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks](https://mlsanity.com/paper/2506.00437/) (2025)
- [Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning](https://mlsanity.com/paper/2410.12657/) (2024)
- [REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretability](https://mlsanity.com/paper/2406.14214/) (2024)
- [From Binary to Continuous: Stochastic Re-Weighting for Robust Graph Explanation](https://mlsanity.com/paper/2508.01925/) (2025)
- [Explainable Graph Representation Learning via Graph Pattern Analysis](https://mlsanity.com/paper/2512.04530/) (2025)
- [Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability](https://mlsanity.com/paper/2606.05756/) (2026)
- [NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption](https://mlsanity.com/paper/2608.16038/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. However, their predictions are often not interpretable. Post-hoc instance-level explanation methods have been proposed to understand GNN predictions. These methods seek to discover substructures that explain the prediction behavior of a trained GNN. In this paper, we shed light on the existence of the distribution shifting issue in existing methods, which affects explanation quality, particularly in applications on real-life datasets with tight decision boundaries. To address this issue, we introduce a generalized Graph Information Bottleneck (GIB) form that includes a label-independent graph variable, which is equivalent to the vanilla GIB. Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue. We conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our proposed mixup approach over existing approaches. We also provide a detailed analysis of how our proposed approach alleviates the distribution shifting issue.