A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
By Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu. IEEE Transactions on Big Data, 2020
454 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/a11828bb8b2e5f1644360567f0e46d20de342ad6
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Citations in the corpus
- Heterogeneous Graph Structure Learning for Graph Neural Networks (2021, background): “The semantic graph is generated depending on the high-order topology structure in HIN, describing the multi-hop structural interactions between two nodes.”
- Beyond Low-frequency Information in Graph Convolutional Networks (2021, background): “Even the raw features, containing both low-and high-frequency information, are alternative solution (Wang et al. 2020b).”
- A Survey of Graph Neural Networks for Social Recommender Systems (2022, background): “Although HetGNN encoders have recently attracted attention in many domains (e.g., healthcare and cybersecurity) [98], there have been only a few attempts to leverage such heterogeneity in SocialRS [8, 94].”