Sufen Zhao1,2, Rong Peng1,*, Po Hu2, Liansheng Tan2
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781
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… More >
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