Graph neural networks (GNNs) have become essential tools for analyzing non-Euclidean data across various domains. During training stage, sampling plays an important role in reducing latency by limiting the number of nodes processed, particularly in large-scale applications. However, as the demand for better prediction performance grows, existing sampling algorithms become increasingly complex, leading to significant overhead. To mitigate this, we propose YOSO (You-Only-Sample-Once), an algorithm designed to achieve efficient training while preserving prediction accuracy. YOSO introduces a compressed sensing (CS)-based sampling and reconstruction framework, where nodes are sampled once at input layer, followed by a lossless reconstruction at the output layer per epoch. By integrating the reconstruction process with the loss function of specific learning tasks, YOSO not only avoids costly computations in traditional compressed sensing (CS) methods, such as orthonormal basis calculations, but also ensures high-probability accuracy retention which equivalent to full node participation. Experimental results on node classification and link prediction demonstrate the effectiveness and efficiency of YOSO, reducing GNN training by an average of 75\% compared to state-of-the-art methods, while maintaining accuracy on par with top-performing baselines.

翻译：图神经网络（GNN）已成为分析跨领域非欧几里得数据的关键工具。在训练阶段，采样通过限制处理的节点数量对降低延迟具有重要作用，尤其在大规模应用中。然而，随着对预测性能要求的提高，现有采样算法日益复杂，导致显著开销。为缓解此问题，我们提出YOSO（You-Only-Sample-Once）算法，旨在保持预测精度的同时实现高效训练。YOSO引入基于压缩感知（CS）的采样与重构框架：节点在输入层仅采样一次，随后在每个训练周期于输出层进行无损重构。通过将重构过程与特定学习任务的损失函数相结合，YOSO不仅避免了传统压缩感知方法中代价高昂的计算（如正交基计算），还保证了与全节点参与等效的高概率精度保持。在节点分类和链接预测任务上的实验结果表明，YOSO在保持与最优基线方法相当精度的同时，平均将GNN训练时间较前沿方法减少75%，验证了其有效性与高效性。