Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the \emph{under-reaching} issue. In this study, we firstly reveal under-reaching by conducting an empirical investigation on various well-known graphs. Then, we demonstrate that under-reaching results in unsatisfactory distribution alignment between labeled and unlabeled nodes through systematic experimental analysis, significantly degrading GNNs' performance. To tackle under-reaching for GNNs, we propose an architecture-agnostic method dubbed NodeMixup. The fundamental idea is to (1) increase the reachability of labeled nodes by labeled-unlabeled pairs mixup, (2) leverage graph structures via fusing the neighbor connections of intra-class node pairs to improve performance gains of mixup, and (3) use neighbor label distribution similarity incorporating node degrees to determine sampling weights for node mixup. Extensive experiments demonstrate the efficacy of NodeMixup in assisting GNNs in handling under-reaching. The source code is available at \url{https://github.com/WeigangLu/NodeMixup}.

翻译：图神经网络（GNNs）已成为解决半监督节点分类问题的主流方法。然而，由于图中标记节点的位置分布不均匀，标记节点仅能覆盖少量未标记节点，导致出现"欠覆盖"问题。本研究首先通过对多种已知图进行实证分析，揭示了欠覆盖现象。随后，通过系统性实验分析证明，欠覆盖会导致标记节点与未标记节点之间的分布对齐效果不佳，从而显著降低GNNs的性能。为解决GNNs中的欠覆盖问题，我们提出了一种与架构无关的方法NodeMixup。其核心思想是：（1）通过标记-未标记节点对的混合增加标记节点的可达性；（2）通过融合同类节点对的邻居连接来利用图结构，提升混合增益的性能优势；（3）利用邻居标签分布相似性结合节点度数，确定节点混合的采样权重。大量实验证明，NodeMixup能有效帮助GNNs应对欠覆盖问题。源代码已在https://github.com/WeigangLu/NodeMixup 公开。