标题： AttriReBoost：一种无梯度传播优化方法，用于属性缺失图中的冷启动缓解 显示英文标题

标题： AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs

摘要： 缺失属性问题在图学习中很普遍，导致图神经网络（GNNs）的结果出现偏差。 依赖特征传播的现有方法容易出现冷启动问题，尤其是在处理属性重置和低度节点时，这会阻碍有效的传播和收敛。 为了解决这些挑战，我们提出了 AttriReBoost (ARB)，一种新的方法，通过结合基于传播的方法来缓解属性缺失图中的冷启动问题。 ARB 通过重新定义初始边界条件并战略性地集成虚拟边来增强全局特征传播，从而提高节点连通性并确保更稳定和高效的收敛。 该方法实现了无梯度的属性重建，计算开销更低。 所提出的方法具有理论基础，其收敛性得到了严格证明。 在多个现实世界基准数据集上的大量实验表明了 ARB 的有效性，在最先进的方法上平均准确率提高了 5.11%。 此外，ARB 展现出显著的计算效率，在单个 GPU 上仅用 16 秒即可处理包含 249 万个节点的大规模图。 我们的代码可在 https://github.com/limengran98/ARB 获取。 显示英文摘要

摘要： 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.