Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs

💻 Official implementation of our ICML 2025 paper: Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs

🧠 Authors: Wenxin Tai, Ting Zhong, Goce Trajcevski, Fan Zhou
📍 Institutions: University of Electronic Science and Technology of China & Iowa State University
🔗 Paper Link 🤖 This repository is maintained by ICDM Lab

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🧩 Overview

TL;DR: This work presents a systematic investigation into the trustworthiness of explanations generated by self-interpretable graph neural networks (GNNs), revealing that redundancy—caused by weak conciseness constraints—is the root cause of explanation inconsistency and its associated inaccuracy.

We show that:

• Redundancy is difficult to eliminate via existing techniques
• A simple ensemble strategy can mitigate its detrimental effects

We validate our findings through extensive experiments across diverse datasets, model architectures, and self-interpretable GNN frameworks, establishing a benchmark for future research.

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📦 Repository Structure

├── assets
├── configs         # configuration
├── criterion.py    # loss function
├── dataloader.py   # load data
├── dataset.py      # process data
├── datasets        # raw dataset
├── explainer.py    # explainer in self-interpretable GNNs (MLP)
├── main.py         # entry
├── model.py        # GNN backbone (GIN/GCN)
├── outputs         # checkpoints/logs
├── README.md
├── run.sh 
└── trainer.py      # train/valid/test

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⚙️ Installation

We recommend creating a fresh Python environment (e.g., with conda):

conda create -n exgnn python=3.9
conda activate exgnn
pip install -r requirements.txt

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📚 Datasets

We evaluate our method on a variety of datasets:

• Synthetic: BA-2MOTIFS
• Molecular: MUTAGENICITY, 3MR, BENZENE

Datasets can be downloaded from Google Drive, place all datasets (e.g., ba_2motifs, benzene, mr, mutag) in the datasets/ folder.

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🏃‍♀️ Quick Start

1. Train self-interpretable GNNs

python main.py --run_time 10 --dataset ba_2motifs --method gsat

2. Evaluate redundancy (SHD and AUC)

python main.py --run_time 10 --dataset ba_2motifs --method gsat --calculate_shd

python main.py --run_time 10 --dataset ba_2motifs --method gsat --test_by_sample_ensemble

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📁 Pretrained Checkpoints

We provide pretrained model checkpoints and training logs for quick evaluation and reproduction.

You can download them from the Releases tab

To use the checkpoint, place it in the outputs/checkpoints/ folder and run:

python main.py --run_time 10 --dataset ba_2motifs --method gsat --calculate_shd
python main.py --run_time 10 --dataset ba_2motifs --method gsat --test_by_sample_ensemble

Place log files (e.g., events.out.tfevents.*) in the outputs/logs/ directory

You can then view them via TensorBoard:

tensorboard --logdir=./outputs/logs

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📌 Citation

If you find this work useful, please cite us:

@inproceedings{tai2025redundancy,
  title     = {Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs},
  author    = {Tai, Wenxin and Zhong, Ting and Trajcevski, Goce and Zhou, Fan},
  booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
  year      = {2025}
}

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📬 Contact

If you have questions or suggestions, feel free to reach out via GitHub Issues or email: wxtai [AT] outlook [DOT] com