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In this study, we focus on the graph representation learning (a.k.a. networkembedding) in attributed graphs. Different from existing embedding methods thattreat the incorporation of graph structure and semantic as the simplecombination of two optimization objectives, we propose a novel semantic graphrepresentation (SGR) method to formulate the joint optimization of the twoheterogeneous sources into a common high-order proximity based framework.Concretely, we first construct an auxiliary weighted graph, where the complexhomogeneous and heterogeneous relations among nodes and attributes in theoriginal graph are comprehensively encoded. Conventional embedding methods thatconsider high-order topology proximities can then be easily applied to thenewly constructed graph to learn the representations of both node and attributewhile capturing the nonlinear high-order intrinsic correlation inside or amonggraph structure and semantic. The learned attribute embeddings can alsoeffectively support some semantic-oriented inference tasks (e.g., semanticcommunity detection), helping to reveal the graph's deep semantic. Theeffectiveness of SGR is further verified on a series of real graphs, where itachieves impressive performance over other baselines.

Semantic Random Walk for Graph Representation Learning in Attributed Graphs