A survey on heterogeneous network representation learning

作者：

Highlights：

• We put forward a novel taxonomy of existing heterogeneous network representation learning algorithms, including path based algorithms, semantic unit based algorithms and other algorithms.

• We introduce and compare the classical heterogeneous network representation learning techniques in detail. Apparently, this survey brings new perspectives to better understand the existing works.

• To promote future research of this field for researchers, we summarize the research challenges and provide the future research directions in heterogeneous network representation learning.

摘要

•We put forward a novel taxonomy of existing heterogeneous network representation learning algorithms, including path based algorithms, semantic unit based algorithms and other algorithms.•We introduce and compare the classical heterogeneous network representation learning techniques in detail. Apparently, this survey brings new perspectives to better understand the existing works.•To promote future research of this field for researchers, we summarize the research challenges and provide the future research directions in heterogeneous network representation learning.

论文关键词：Heterogeneous network,Network representation learning,Machine learning

论文评审过程：Received 23 December 2019, Revised 26 April 2020, Accepted 5 March 2021, Available online 10 March 2021, Version of Record 18 March 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.107936