Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited. In this paper, we propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs. In previous work, there are explorations of graph semantics via meta-paths. However, these methods mainly rely on explicit heterogeneous information that is hard to be obtained in a large amount of graph-structured data. SGCN first breaks through this restriction via leveraging the semantic-paths dynamically and automatically during the node aggregating process. To evaluate our idea, we conduct sufficient experiments on several standard datasets, and the empirical results show the superior performance of our model.

翻译：图形革命网络中的现有代表学习方法主要通过将每个节点的周围描述为一个概念性的整体来设计,而图表高度复杂的相互作用背后的隐含语义协会基本上没有被利用。在本文中,我们提议建立一个语义结构网络(SGCN),通过在图表中学习潜在的语义路径来探索隐含的语义。在以往的工作中,通过元路径对图形语义学进行探索。然而,这些方法主要依靠在大量图表结构化数据中难以获得的清晰的多种信息。SGCN首先通过在节点集成过程中动态地和自动地利用语义路径来打破这一限制。为了评估我们的想法,我们在若干标准数据集上进行了充分的实验,实验结果显示了我们模型的优异性表现。