From Reliable Explanations to Better Learning: Bridging the OOD Gap in 3D GNN Explanation

Graph neural network (GNN) explainers aim to identify subgraphs that preserve model predictions and have been widely studied on 3D graphs. However, two fundamental questions remain open: (1) Are these explanations reliable? and (2) What benefits can they bring to model learning? In this paper, we show that these two questions are inherently connected through an overlooked issue in 3D GNNs: explanatory subgraphs are fundamentally out-of-distribution (OOD). Due to the cutoff-based construction of 3D proximity graphs, existing explainers break the underlying geometric constraints, producing subgraphs that violate the training distribution. As a result, the predictor behaves unreliably on such inputs, leading to uninformative or even misleading explanations. To address this, we propose E2L, an explanation-aware framework that bridges the OOD gap by aligning the training and explanation distributions. By integrating the explainer into the learning process, the model is directly optimized on the explanatory distribution, ensuring that explanations are in-distribution and reliable for the predictor. Building on this foundation, we further bridge the gap between explanation and representation learning, demonstrating that reliable explanations naturally induce compact and expressive subgraph structures that provide both efficiency and expressivity benefits for representation learning. Experiments on both small and large-scale 3D molecular graph benchmarks show that E2L overcomes the OOD issue, improves explanation reliability, and achieves substantial efficiency and expressivity gains. The code is released at https://anonymous.4open.science/r/E2L-28EE.