Open Access
REVIEW
Heterogeneous Network Embedding: A Survey
Sufen Zhao1,2, Rong Peng1,*, Po Hu2, Liansheng Tan2
1
School of Computer Science, Wuhan University, Wuhan, 430072, China
2
School of Computer Science, Central China Normal University, Wuhan, 430079, China
* Corresponding Author: Rong Peng. Email:
Computer Modeling in Engineering & Sciences 2023, 137(1), 83-130. https://doi.org/10.32604/cmes.2023.024781
Received 08 June 2022; Accepted 06 December 2022; Issue published 23 April 2023
Abstract
Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and
relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional
embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit
various HIN-based machine learning tasks such as node classification, recommender systems, and information
retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define
an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize
various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories
to identify more potentially competitive HNE frameworks. We also summarize the application fields, benchmark
datasets, open source tools, and performance evaluation in the HNE area. Finally, we discuss open issues and suggest
promising future directions. We anticipate that this survey will provide deep insights into research in the field
of HNE.
Graphical Abstract
Keywords
Cite This Article
APA Style
Zhao, S., Peng, R., Hu, P., Tan, L. (2023). Heterogeneous network embedding: A survey. Computer Modeling in Engineering & Sciences, 137(1), 83-130. https://doi.org/10.32604/cmes.2023.024781
Vancouver Style
Zhao S, Peng R, Hu P, Tan L. Heterogeneous network embedding: A survey. Comput Model Eng Sci. 2023;137(1):83-130 https://doi.org/10.32604/cmes.2023.024781
IEEE Style
S. Zhao, R. Peng, P. Hu, and L. Tan "Heterogeneous Network Embedding: A Survey," Comput. Model. Eng. Sci., vol. 137, no. 1, pp. 83-130. 2023. https://doi.org/10.32604/cmes.2023.024781