Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known labels for computing the classification loss at the output. In recent years, several methods have been designed to additionally utilize the labels at the input. One part of the methods augment the node features via concatenating or adding them with the one-hot encodings of labels, while other methods optimize the graph structure by assuming neighboring nodes tend to have the same label. To bring into full play the rich information of labels, in this paper, we present a label-enhanced learning framework for GNNs, which first models each label as a virtual center for intra-class nodes and then jointly learns the representations of both nodes and labels. Our approach could not only smooth the representations of nodes belonging to the same class, but also explicitly encode the label semantics into the learning process of GNNs. Moreover, a training node selection technique is provided to eliminate the potential label leakage issue and guarantee the model generalization ability. Finally, an adaptive self-training strategy is proposed to iteratively enlarge the training set with more reliable pseudo labels and distinguish the importance of each pseudo-labeled node during the model training process. Experimental results on both real-world and synthetic datasets demonstrate our approach can not only consistently outperform the state-of-the-arts, but also effectively smooth the representations of intra-class nodes.
翻译:在半监督节点分类任务中广泛应用了神经网图(GNNs), 关键点在于如何充分利用有限但有价值的标签信息。 大多数古典GNNs都只使用已知的标签来计算输出的分类损失。 近年来, 设计了几种方法来额外使用输入的标签。 部分方法通过连接或添加标签的一热编码来增加节点特性, 而其他方法则通过假设相邻节点往往具有相同的标签来优化图形结构。 为了让标签的丰富信息充分发挥作用, 多数古典 GNNs 仅使用已知的标签标签计算输出损失。 近些年来, 设计了几种方法中的第一个模型作为内部节点和标签的虚拟中心, 然后共同学习了节点和标签的表达方式。 我们的方法不仅可以使属于同一类的节点的表达方式更加平滑, 还可以将标签的语义结构结构优化为 GNNNPs的学习过程。 此外, 培训节点选择的精度显示标签的丰富信息, 最终, 将模型选择策略的精确性 提升自我定位, 测试能力 将最终 用于消除了整个标签的自我分析 。