One significant challenge of exploiting Graph neural networks (GNNs) in real-life scenarios is that they are always treated as black boxes, therefore leading to the requirement of interpretability. Model-level interpretations explain what patterns maximize probability of predicting to a certain class. However, existing model-level interpretation methods pose several limitations such as generating invalid explanation graphs and requiring extreme fine-tuning on hyperparameters manually. In this paper, we propose a new Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks (GIN-Graph), to generate reliable model-level explanation graphs. The implicit and likelihood-free generative adversarial networks are exploited to construct explanation graphs similar to original graphs, meanwhile maximizing the prediction probability for a certain class by adopting a novel objective function. Experimental results indicate that GIN-Graph can be easily applied to GNN models trained on a variety of graph datasets to create meaningful explanation graphs without requiring extensive fine-tuning on hyperparameters.
View on arXiv@article{yue2025_2503.06352,
title={ GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks },
author={ Xiao Yue and Guangzhi Qu and Lige Gan },
journal={arXiv preprint arXiv:2503.06352},
year={ 2025 }
}