In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation (SGR) method to formulate the joint optimization of the two heterogeneous sources into a common high-order proximity based framework. Concretely, we first construct an auxiliary weighted graph, where the complex homogeneous and heterogeneous relations among nodes and attributes in the original graph are comprehensively encoded. Conventional embedding methods that consider high-order topology proximities can then be easily applied to the newly constructed graph to learn the representations of both node and attribute while capturing the nonlinear high-order intrinsic correlation inside or among graph structure and semantic. The learned attribute embeddings can also effectively support some semantic-oriented inference tasks (e.g., semantic community detection), helping to reveal the graph's deep semantic. The effectiveness of SGR is further verified on a series of real graphs, where it achieves impressive performance over other baselines.

翻译：本研究聚焦于属性图中的图表示学习（亦称网络嵌入）。与现有将图结构与语义的融合简单视为两个优化目标的组合的嵌入方法不同，我们提出了一种新颖的语义图表示方法，将两种异质源的联合优化统一到基于高阶邻近性的通用框架中。具体而言，我们首先构建一个辅助加权图，其中原始图中节点与属性间复杂的同质与异质关系被全面编码。随后，考虑高阶拓扑邻近性的传统嵌入方法可轻松应用于新构建的图，以同时学习节点与属性的表示，同时捕获图结构与语义内部或二者之间的非线性高阶内在关联。学习到的属性嵌入还能有效支持语义导向的推理任务（如语义社区检测），有助于揭示图的深层语义。在多个真实图数据集上，SGR的有效性得到进一步验证，其性能显著优于其他基线方法。