Low-Rank Approximation for Graph Neural Networks: A Survey

In this paper, we explore a new approach to verify the quality of explanations generated by GNNExplainer, a popular tool for explaining Graph Neural Networks (GNNs). Explanations in AI are crucial as they help us understand how machines make decisions, especially in complex relational data. However, evaluating these explanations is challenging due to their inherent complexity. To address this challenge, we develop a method that relies on symmetric approximations of the original data and uses factor graph models to quantify uncertainty in an explanation.
Our approach involves generating counterfactual examples based on the explanations generated by GNNExplainer. We then use these examples to learn a factor graph model that estimates the uncertainty in each explanation. Our results show that our method can reliably estimate the uncertainty of a relation specified in the explanation, which is essential for verifying the quality of an explanation.
To explain this further, imagine you are trying to understand how a machine learning model arrived at a particular prediction. Explanations generated by GNNExplainer can help you understand the reasoning behind the prediction, but it’s essential to verify their quality to ensure they are accurate and meaningful. Our approach provides a way to do this by estimating the uncertainty in each explanation, giving you a better understanding of how confident you should be in the explanation.
In summary, our paper presents a new method for verifying the quality of explanations generated by GNNExplainer, which is essential for building trust in AI systems that rely on these explanations. By using symmetric approximations and factor graph models, we provide a robust way to estimate the uncertainty in each explanation, giving you a more accurate understanding of how the machine learning model arrived at its prediction.