Semantic Tradeoff for Heterogeneous Graph Embedding

Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, which needs to be a feasible and effective tradeoff for high-quality HG embedding, yet studies thereof have rarely been reported. In this article, we propose semantic tradeoff HG embedding (STHGE) by first introducing the Hilbert–Schmidt independence criterion (HSIC) as restriction. The main idea of STHGE is to regard semantic tradeoff as independence tradeoff (or correlation) between different meta-path spaces. Specifically, we first transform the original features of nodes into different meta-path feature spaces with HSIC restriction between them. Then, we use graph attention network to learn the embeddings of nodes on different meta-paths with HSIC restrictions. Finally, we concatenate the embeddings on different meta-paths to perform prediction. Experimental results on three heterogeneous datasets not only demonstrate the effectiveness of STHGE but also demonstrate that STHGE can achieve a new semantic tradeoff between different meta-paths. Furthermore, we demonstrate the robustness of STHGE.