  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.
