Graph Convolutional Networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labeled nodes used by GCNs may lead to unstable generalization performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labeled nodes: the Determinate Node Selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph: typical nodes and divergent nodes. These labeled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation, as compared to the original method.

翻译：在半监督节点分类领域,通过从图形数据中提取结构信息,图表相联网络(GCNs)已证明在半监督节点分类领域是成功的。然而,随机选择GCNs使用的标签节点可能导致GCNs不稳的一般性能。在本文中,我们建议了一种有效的方法,用于确定标签节点的选择:确定节点选择(DNS)算法。DNS算法在图形中确定了两类有代表性的节点:典型节点和不同节点。这些有标签的节点是通过探索图形结构以及确定节点代表图内数据分布的能力来选择的。DNS算法可以非常简单地应用于广泛的半监督的图形神经网络模型,用于节点分类任务。我们通过广泛的实验,已经证明DNS算法的结合导致模型平均精度的显著提高,与原始方法相比,标准偏差显著下降。