计算机科学与探索2019，Vol.13Issue(7)：1082-1094,13.
面向异质信息网络的表示学习方法研究综述
Survey on Representation Learning Methods Oriented to Heterogeneous Information Network*
摘要
Abstract
Network representation learning aims to learn a series of low-dimensional vectors for the components (node, edge, subgraph, etc.) in a network. Meanwhile, the characters of the components in the original network should be largely retained in these vectors. Heterogeneous information network is the network composed of various types of nodes, link relationships and attribute information. It is characterized by dynamics, large scale and heteroge-neity, and is ubiquitous in the real life. Network representation learning by integrating various heterogeneous infor-mation can not only alleviate the problem of data sparsity, but also help to learn the representation vectors with high discriminative and inferential ability. At the same time, it also faces the challenge of dealing with complex data rela-tionships and balancing heterogeneous information. In recent years, researchers have designed different representation learning algorithms for heterogeneous information networks, which have greatly promoted the development of this field. In view of these algorithms, this paper first designs a unified classification framework, then generalizes and compares the representative algorithms in each category, including their time complexities, advantages, etc. In addition, the information of the commonly used data sets is summarized into a table. Some challenges and possible research directions are provided at the end of this paper.关键词
网络表示学习/异质信息网络/网络分析Key words
network representation learning/ heterogeneous information network/ network analysis分类
信息技术与安全科学引用本文复制引用
ZHOU Hui,ZHAO Zhongying+,LI Chao..面向异质信息网络的表示学习方法研究综述[J].计算机科学与探索,2019,13(7):1082-1094,13.基金项目
The National Natural Science Foundation of China under Grant Nos. 61303167, 61433012 (国家自然科学基金) （国家自然科学基金）
the Foundation for Humanities and Social Sciences of Ministry of Education of China under Grant No. 17YJCZH262 (教育部人文社会科学研究项目) （教育部人文社会科学研究项目）
the Natural Science Foundation of Shandong Province under Grant No. ZR2018BF013 (山东省自然科学基金) （山东省自然科学基金）
the Innovative Research Foundation of Qingdao under Grant No. 18-2-2-41-jch (青岛市源头创新计划应用基础研究项目). （青岛市源头创新计划应用基础研究项目）
