A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

TL;DR

This survey comprehensively reviews recent methods for heterogeneous graph embedding, discussing their techniques, applications, challenges, and future directions to facilitate research and industrial deployment.

Contribution

It systematically categorizes and analyzes state-of-the-art HG embedding methods, highlighting their pros, cons, and applicability in real-world industrial environments.

Findings

HG embedding methods vary based on information used in learning

Several systems successfully apply HG embedding to real-world problems

Open-source tools and benchmarks support future research

Abstract

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.