An Adaptive Embedding Framework for Heterogeneous Information Networks

Heterogeneous information networks (HINs) have been ubiquitous in the real-world. HIN embeddings, which encode various information of the networks into low-dimensional vectors, can facilitate a wide range of applications on graph-structured data. Existing HIN embedding methods include random walk based methods that may not fully utilize the edge semantics and knowledge graph embedding methods that restrict the expression ability of topological information. In this paper, we propose a novel adaptive embedding framework, which integrates these two kinds of methods to preserve both topological information and relational information. By incorporating an assistant knowledge graph embedding model, the proposed framework performs efficient biased random walk under the guidance of edge semantics. Meanwhile, the short and long dependency information can be adaptively preserved for diverse networks. Furthermore, a patient joint training strategy is proposed to make the framework flexible and adapt to different assistant knowledge graph embedding models. Extensive experiments on five real-world datasets demonstrate that the proposed method can adaptively sample node contexts and outperforms several state-of-the-art methods for node classification and link prediction tasks.