为无监督图神经网络生成可解释的反事实解释，提升模型可信度。
UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
- 基于k近邻变化识别关键子图，生成反事实解释。
- 在多种数据集上优于现有方法，支持链接预测与聚类任务。
- 适用于希望理解无监督图学习模型决策过程的研究者。
节点表示学习（如图神经网络）已成为机器学习的关键方法。然而，对可靠解释生成的需求日益增长，而无监督模型仍缺乏充分探索。为此，我们提出一种针对无监督节点表示学习的反事实（CF）解释生成方法。该方法通过扰动识别导致目标节点在嵌入空间中k近邻发生显著变化的关键子图。基于k近邻的反事实解释为理解无监督下游任务（如top-k链接预测和聚类）提供了简洁而关键的信息。因此，我们提出了UNR-Explainer，基于蒙特卡洛树搜索（MCTS）生成表达性强的无监督节点表示学习反事实解释。该方法在多种数据集上对无监督GraphSAGE和DGI均表现出优越性能。
原文摘要 · Abstract (English)
Node representation learning, such as Graph Neural Networks (GNNs), has emerged as a pivotal method in machine learning. The demand for reliable explanation generation surges, yet unsupervised models remain underexplored. To bridge this gap, we introduce a method for generating counterfactual (CF) explanations in unsupervised node representation learning. We identify the most important subgraphs that cause a significant change in the k-nearest neighbors of a node of interest in the learned embedding space upon perturbation. The k-nearest neighbor-based CF explanation method provides simple, yet pivotal, information for understanding unsupervised downstream tasks, such as top-k link prediction and clustering. Consequently, we introduce UNR-Explainer for generating expressive CF explanations for Unsupervised Node Representation learning methods based on a Monte Carlo Tree Search (MCTS). The proposed method demonstrates superior performance on diverse datasets for unsupervised GraphSAGE and DGI.
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