@Article{cmes.2023.024781,
AUTHOR = {Sufen Zhao, Rong Peng, Po Hu, Liansheng Tan},
TITLE = {Heterogeneous Network Embedding: A Survey},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {1},
PAGES = {83--130},
URL = {http://www.techscience.com/CMES/v137n1/52320},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.024781}
}