Previous studies have demonstrated the strong performance of Graph Neural Networks (GNNs) in node classification. However, most existing GNNs adopt a node-centric perspective and rely on global message passing, leading to high computational and memory costs that hinder scalability. To mitigate these challenges, subgraph-based methods have been introduced, leveraging local subgraphs as approximations of full computational trees. While this approach improves efficiency, it often suffers from performance degradation due to the loss of global contextual information, limiting its effectiveness compared to global GNNs. To address this trade-off between scalability and classification accuracy, we reformulate the node classification task as a subgraph classification problem and propose SubGND (Subgraph GNN for NoDe). This framework introduces a differentiated zero-padding strategy and an Ego-Alter subgraph representation method to resolve label conflicts while incorporating an Adaptive Feature Scaling Mechanism to dynamically adjust feature contributions based on dataset-specific dependencies. Experimental results on six benchmark datasets demonstrate that SubGND achieves performance comparable to or surpassing global message-passing GNNs, particularly in heterophilic settings, highlighting its effectiveness and scalability as a promising solution for node classification.

翻译：先前的研究已证明图神经网络（GNNs）在节点分类任务中表现出色。然而，现有的大多数GNN采用以节点为中心的视角，并依赖于全局消息传递，导致较高的计算和内存成本，从而阻碍了可扩展性。为缓解这些挑战，基于子图的方法被引入，其利用局部子图作为完整计算树的近似。尽管这种方法提高了效率，但由于全局上下文信息的丢失，其性能往往下降，限制了其相对于全局GNN的有效性。为解决可扩展性与分类精度之间的权衡，我们将节点分类任务重新表述为子图分类问题，并提出了SubGND（用于节点的子图GNN）。该框架引入了差异化的零填充策略和Ego-Alter子图表示方法，以解决标签冲突，同时结合自适应特征缩放机制，根据数据集特定的依赖关系动态调整特征贡献。在六个基准数据集上的实验结果表明，SubGND实现了与全局消息传递GNN相当甚至更优的性能，尤其是在异配性设置下，突显了其作为节点分类解决方案的有效性和可扩展性。