We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce unsupervised kernel machine layers propagating the node features in a one-hop neighborhood, using implicit node feature mappings. (ii) We specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. We derive an effective initialization scheme and efficient end-to-end training algorithm in the dual variables for the full architecture. The main idea underlying GCKM is that, because of the unsupervised core, the final model can achieve higher performance in semi-supervised node classification when few labels are available for training. Experimental results demonstrate the effectiveness of the proposed framework.

翻译：我们提出了一种深度图卷积核机（GCKM），用于图数据中的半监督节点分类。该方法由两种主要模块构成：(i) 我们引入无监督核机器层，利用隐式节点特征映射在单跳邻域内传播节点特征。(ii) 我们通过Fenchel-Young不等式视角构建了一个半监督分类核机器。我们推导了该全架构在对偶变量中的有效初始化方案和高效的端到端训练算法。GCKM的核心思想在于，由于采用无监督核心，在训练标签稀缺时，最终模型在半监督节点分类中能够取得更高性能。实验结果验证了所提框架的有效性。