Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach
This addresses scalability issues in graph neural networks for node classification, offering a promising solution for large-scale or heterophilic graphs, though it is an incremental improvement over existing subgraph methods.
The paper tackles the trade-off between scalability and accuracy in node classification by proposing SubGND, a subgraph-based GNN that reformulates the task as subgraph classification, achieving performance comparable to or surpassing global GNNs on six benchmark datasets, especially in heterophilic settings.
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.