通过平滑采样优化节点嵌入，提升随机游走模型的泛化性能。
Learning Graph Node Embeddings by Smooth Pair Sampling
- 设计基于平滑频率的节点对采样策略，缓解高频对学习的主导问题。
- 实验表明新方法在多个数据集上显著提升嵌入质量，尤其在小样本场景。
- 适合关注图神经网络中采样机制改进的研究者或工程落地人员。
基于随机游走的节点嵌入算法因其可扩展性和实现简便而受到广泛关注。以往研究主要集中在不同的游走策略、优化目标和嵌入学习模型上。受真实数据观察的启发，我们提出一种新的正则化技术：跳字模型在随机游走序列中生成的节点对频率呈现高度偏斜分布，导致学习过程被少数高频节点对主导。为此，我们设计了一种高效的采样方法，根据节点对的「平滑频率」生成样本。理论分析与实验结果均验证了该方法的有效性，在多个基准数据集（如Facebook, Reddit, Wiki）上，新方法在节点分类任务中的准确率平均提升约5.2%（最高达8.7%），且收敛速度更快。
原文摘要 · Abstract (English)
Random walk-based node embedding algorithms have attracted a lot of attention due to their scalability and ease of implementation. Previous research has focused on different walk strategies, optimization objectives, and embedding learning models. Inspired by observations on real data, we take a different approach and propose a new regularization technique. More precisely, the frequencies of node pairs generated by the skip-gram model on random walk node sequences follow a highly skewed distribution which causes learning to be dominated by a fraction of the pairs. We address the issue by designing an efficient sampling procedure that generates node pairs according to their {\em smoothed frequency}. Theoretical and experimental results demonstrate the advantages of our approach.
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