  Heterogeneous graphs (HGs) also known as heterogeneous information networks
have become ubiquitous in real-world scenarios; therefore, HG embedding, which
aims to learn representations in a lower-dimension space while preserving the
heterogeneous structures and semantics for downstream tasks (e.g., node/graph
classification, node clustering, link prediction), has drawn considerable
attentions in recent years. In this survey, we perform a comprehensive review
of the recent development on HG embedding methods and techniques. We first
introduce the basic concepts of HG and discuss the unique challenges brought by
the heterogeneity for HG embedding in comparison with homogeneous graph
representation learning; and then we systemically survey and categorize the
state-of-the-art HG embedding methods based on the information they used in the
learning process to address the challenges posed by the HG heterogeneity. In
particular, for each representative HG embedding method, we provide detailed
introduction and further analyze its pros and cons; meanwhile, we also explore
the transformativeness and applicability of different types of HG embedding
methods in the real-world industrial environments for the first time. In
addition, we further present several widely deployed systems that have
demonstrated the success of HG embedding techniques in resolving real-world
application problems with broader impacts. To facilitate future research and
applications in this area, we also summarize the open-source code, existing
graph learning platforms and benchmark datasets. Finally, we explore the
additional issues and challenges of HG embedding and forecast the future
research directions in this field.
