提出自适应高阶邻居融合机制，解决大图神经网络的效率与过平滑问题。
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion
- 按跳数分离结构信号，轻量融合高低阶特征
- 通过贡献度掩码剪枝低相关高阶邻居，减少冗余
- 适合大规模图学习，提升精度与推理速度
图神经网络（GNN）通过消息传递捕捉复杂节点关系，在多种图任务中表现优异。但在真实世界的大规模图上，面临两大挑战：（1）重复聚合大范围邻居导致显著计算开销，影响可扩展性；（2）过度传播引发过平滑，使节点表示趋于一致，削弱模型表达能力。为此，我们提出ScaleGNN，通过自适应融合多跳邻居特征实现高效且有效的图学习。首先计算各跳的纯邻居矩阵以分离专属结构信号，再通过轻量级融合平衡低阶与高阶信息，保留局部细节与全局关联。为抑制冗余和过平滑，引入基于局部贡献度（LCS）的掩码机制，剔除低相关高阶邻居，并施加可学习稀疏性，有选择地整合有价值的多跳特征。在真实数据集上的大量实验表明，ScaleGNN在预测精度和计算效率方面均持续优于当前最先进GNN模型。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However, on large-scale real-world graphs, GNNs face two major challenges: (1) GNNs struggle to ensure scalability and efficiency as repeated aggregation of large neighborhoods incurs significant computational overhead; (2) GNNs suffer from over-smoothing, where excessive propagation makes node representations indistinguishable, hindering model expressiveness. To tackle these, we propose ScaleGNN, which adaptively fuses multi-hop node features for scalable and effective graph learning. We first compute per-hop pure-neighbor matrices to isolate exclusive structural signals, then apply lightweight fusion to balance low- and high-order information, preserving both local detail and global correlations. To curb redundancy and over-smoothing, we introduce Local Contribution Score (LCS)-based masking to prune low-relevance high-order neighbors, and impose learnable sparsity to selectively integrate valuable multi-hop features. Extensive experiments on real-world datasets show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency.
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