In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these applications is the underutilization of crucial structural information. To address this problem, we propose the Structural-Clustering PageRank method for improved Active learning (SPA) specifically designed for graph-structured data. SPA integrates community detection using the SCAN algorithm with the PageRank scoring method for efficient and informative sample selection. SPA prioritizes nodes that are not only informative but also central in structure. Through extensive experiments, SPA demonstrates higher accuracy and macro-F1 score over existing methods across different annotation budgets and achieves significant reductions in query time. In addition, the proposed method only adds two hyperparameters, $\epsilon$ and $\mu$ in the algorithm to finely tune the balance between structural learning and node selection. This simplicity is a key advantage in active learning scenarios, where extensive hyperparameter tuning is often impractical.

提出了面向图结构数据的改进型主动学习方法——结构聚类 PageRank 方法 (SPA)，它通过集成 SCAN 算法的社区检测与 PageRank 评分方法，实现了高效且信息丰富的样本选择，SPA 能够优先选择既具有信息量又在结构上具有重要性的节点，经过大量实验证明，SPA 在不同的注释预算下展现出更高的准确性和宏观 F1 分数，并且显著减少了查询时间，且该方法只引入了两个超参数，即 ε 和 μ，在算法中可以精细调整结构学习与节点选择之间的平衡，简洁性是主动学习场景中的一个重要优势，因为其中大量超参数调优往往是不切实际的。

基于结构聚类的图神经网络主动学习