ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion (2504.15920v3)

Abstract: Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However, when applied to large-scale real-world graphs, GNNs face two major challenges: First, it becomes increasingly difficult to ensure both scalability and efficiency, as the repeated aggregation of large neighborhoods leads to significant computational overhead; Second, the over-smoothing problem arises, where excessive or deep propagation makes node representations indistinguishable, severely hindering model expressiveness. To tackle these issues, we propose ScaleGNN, a novel framework that adaptively fuses multi-level graph features for both scalable and effective graph learning. ScaleGNN first constructs per-order neighbor matrices that capture only the exclusive structural information at each hop, avoiding the redundancy of conventional aggregation. A learnable fusion module then selectively integrates these features, emphasizing the most informative high-order neighbors. To further reduce redundancy and over-smoothing, we introduce a Local Contribution Score (LCS)-based masking mechanism to filter out less relevant high-order neighbors, ensuring that only the most meaningful information is aggregated. In addition, a task-aware feature fusion strategy dynamically balances low- and high-order information, preserving both local detail and global context without incurring excessive complexity. Extensive experiments on real-world datasets demonstrate that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency, highlighting its practical value for large-scale graph learning.