UM-Explainer: An Explainability Method for Unsupervised Models Based on Factual and Counterfactual Reasoning

Graph neural networks (GNNs) have shown significant advantages in learning structured data representations and have become one of the important methods in machine learning. However, the inherent opacity of deep learning models hinders their widespread application in critical domains, making the explanation of GNNs predictions particularly crucial. While explanations for supervised learning models have been extensively studied, research on generating explanations for unsupervised learning models remains relatively scarce. Current explanation methods for unsupervised models mostly focus on either factual reasoning or counterfactual reasoning. But relying on a single reasoning approach often results in explanations that are either redundant or incomplete. To address this challenge, this paper proposes a novel explanation method for Unsupervised Models named UM-Explainer, which is based on factual and counter-factual reasoning. By calculating the distance between cluster centers and the example to be explained, a contrastive loss based on both factual and counterfactual reasoning is constructed to optimize the mask, thereby generating an ideal explanatory subgraph. To validate the effectiveness of UM-Explainer, we conducted experiments with synthetic and real datasets on multiple unsupervised models. Experimental results demonstrate that UM-Explainer performs outstandingly in generating accurate explanations.