Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks. However, the inference process is often not interpretable. Most existing explanation techniques are limited to understanding GNN behaviors in classification tasks. In this work, we seek an explanation to interpret the graph regression models (XAIG-R). We show that existing methods overlook the distribution shifting and continuously ordered decision boundary, which hinders them away from being applied in the regression tasks. To address these challenges, we propose a novel objective based on the information bottleneck theory and introduce a new mix-up framework, which could support various GNNs in a model-agnostic manner. We further present a contrastive learning strategy to tackle the continuously ordered labels in regression task. To empirically verify the effectiveness of the proposed method, we introduce three benchmark datasets and a real-life dataset for evaluation. Extensive experiments show the effectiveness of the proposed method in interpreting GNN models in regression tasks.

翻译：图回归是一项基础性任务，在广泛的图学习领域中日益受到关注。然而，其推理过程通常缺乏可解释性。现有的大多数解释技术局限于理解分类任务中的图神经网络（GNN）行为。本研究旨在寻求一种能够解释图回归模型（XAIG-R）的方法。我们发现，现有方法忽略了分布偏移与连续有序的决策边界，这阻碍了它们应用于回归任务。为应对这些挑战，我们基于信息瓶颈理论提出了一种新的目标函数，并引入了一个全新的混合框架，该框架能以模型无关的方式支持多种GNN。此外，我们进一步提出了一种对比学习策略，以处理回归任务中连续有序的标签。为了实证验证所提方法的有效性，我们引入了三个基准数据集和一个真实场景数据集进行评估。大量实验表明，所提方法在解释回归任务中的GNN模型方面具有显著成效。