GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks

• URL: http://arxiv.org/abs/2503.06352v2
• Date: Thu, 18 Sep 2025 21:44:47 GMT
• Title: GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks
• Authors: Xiao Yue, Guangzhi Qu, Lige Gan,
• Abstract summary: We propose a new Generative Network for Model-Level Explanation of Graph Neural Networks (GIN-Graph)<n>GIN-Graph generates reliable and high-quality model-level explanation graphs.<n> Experimental results indicate that GIN-Graph can be applied to interpret GNNs trained on a variety of graph datasets.
• Score: 0.5702263832571335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. To address this, model-level interpretation methods have been developed to 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 lacking reliability. In this paper, we propose a new Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks (GIN-Graph), to generate reliable and high-quality model-level explanation graphs. The implicit and likelihood-free generative adversarial networks are exploited to construct the explanation graphs which are similar to original graphs, meanwhile maximizing the prediction probability for a certain class by adopting a novel objective function for generator with dynamic loss weight scheme. Experimental results indicate that GIN-Graph can be applied to interpret GNNs trained on a variety of graph datasets and generate high-quality explanation graphs with high stability and reliability.

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