  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.
