We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup. We propose GraphPart, a novel partition-based active learning approach for GNNs. GraphPart first splits the graph into disjoint partitions and then selects representative nodes within each partition to query. The proposed method is motivated by a novel analysis of the classification error under realistic smoothness assumptions over the graph and the node features. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method outperforms existing active learning methods for GNNs under a wide range of annotation budget constraints. In addition, the proposed method does not introduce additional hyperparameters, which is crucial for model training, especially in the active learning setting where a labeled validation set may not be available.

翻译：我们研究了在主动学习框架下使用图神经网络（GNNs）进行半监督学习的问题。我们提出了GraphPart，一种新颖的基于分区的图神经网络主动学习方法。GraphPart首先将图划分为互不相交的分区，然后在每个分区内选择代表性节点进行标注。该方法的提出源于对图及节点特征在现实平滑假设下分类误差的理论分析。在多个基准数据集上的大量实验表明，在多种标注预算约束下，所提方法优于现有的图神经网络主动学习方法。此外，该方法不引入额外超参数，这对模型训练至关重要，尤其在可能无法获得标注验证集的主动学习场景中。