标题： 图神经网络的节点特征传播主动学习 显示英文标题

标题： Active Learning for Graph Neural Networks via Node Feature Propagation

摘要： 图神经网络（GNNs）在节点分类或边预测等预测任务上已经引起了近期从图形结构数据中进行机器学习的越来越多的关注。然而，大量的标记图很难获得，这极大地限制了GNNs的实际成功。尽管主动学习已经被广泛研究用于解决文本、图像等其他数据类型的标签稀疏问题，但如何使其在图上有效仍是一个研究的开放问题。本文探讨了使用GNNs进行主动学习以解决节点分类任务的问题。具体来说，我们提出了一种新方法，该方法使用节点特征传播，随后对节点进行K-Medoids聚类，以实现主动学习中的实例选择。通过理论界限分析，我们证明了我们方法的设计选择。在四个基准数据集上的实验表明，所提出的方法始终且显著地优于其他代表性基线方法。 显示英文摘要

摘要： Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present an investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning. With a theoretical bound analysis we justify the design choice of our approach. In our experiments on four benchmark datasets, the proposed method outperforms other representative baseline methods consistently and significantly.