不依赖梯度的图属性补全方法，解决冷启动与低度节点难题
AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs
- 通过重构边界条件和添加虚拟边增强全局传播
- 在真实数据集上平均准确率提升5.11%，大图仅需16秒
- 适合处理属性缺失、节点度数低的复杂图结构
图学习中属性缺失问题普遍存在，导致图神经网络结果偏差。现有基于特征传播的方法在属性重置和低度节点场景下易陷入冷启动困境，阻碍有效传播与收敛。为此，我们提出 AttriReBoost（ARB），一种创新的无梯度传播优化方法，通过重构初始边界条件并战略性引入虚拟边，增强节点连通性，提升全局特征传播能力，实现更稳定高效的收敛。该方法支持无需梯度的属性重建，计算开销更低。理论分析证明其收敛性。大量实验证明，ARB 在多个真实世界基准数据集上表现优异，平均准确率较当前最优方法提升5.11%；在单块GPU上处理包含249万节点的大规模图仅耗时16秒。代码已开源。
原文摘要 · Abstract (English)
Missing attribute issues are prevalent in the graph learning, leading to biased outcomes in Graph Neural Networks (GNNs). Existing methods that rely on feature propagation are prone to cold start problem, particularly when dealing with attribute resetting and low-degree nodes, which hinder effective propagation and convergence. To address these challenges, we propose AttriReBoost (ARB), a novel method that incorporates propagation-based method to mitigate cold start problems in attribute-missing graphs. ARB enhances global feature propagation by redefining initial boundary conditions and strategically integrating virtual edges, thereby improving node connectivity and ensuring more stable and efficient convergence. This method facilitates gradient-free attribute reconstruction with lower computational overhead. The proposed method is theoretically grounded, with its convergence rigorously established. Extensive experiments on several real-world benchmark datasets demonstrate the effectiveness of ARB, achieving an average accuracy improvement of 5.11% over state-of-the-art methods. Additionally, ARB exhibits remarkable computational efficiency, processing a large-scale graph with 2.49 million nodes in just 16 seconds on a single GPU. Our code is available at https://github.com/limengran98/ARB.
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