摘要
实际系统往往由大量类型各异、彼此交互的组件构成.目前,大多数工作将这些交互系统建模为同质信息网络,并未考虑不同类型对象的复杂异质交互关系,因而造成大量信息损失.近年来,越来越多的研究者将这些交互数据建模为由不同类型节点和边构成的异质信息网络,从而利用网络中全面的结构信息和丰富的语义信息进行更精准的知识发现.特别是随着大数据时代的到来,异质信息网络能够自然融合异构多源数据的优势使其成为解决大数据多样性的重要途径.因此,异质信息网络分析迅速成为数据挖掘研究和产业应用的热点.对异质信息网络分析与应用进行了全面的综述.除了介绍异质信息网络领域的基本概念外,重点聚焦基于异质网络元路径的数据挖掘方法、异质信息网络的表示学习技术和实际应用这3个方面的最新研究进展,并对未来的发展方向进行了展望.
The real-world systems usually contain different types of components that interact with each other.Most existing work models these interaction systems as homogeneous information networks,which does not considerthe heterogeneous interaction relationships among objects,resulting in lots of information loss.In recent years,more researchers model these interaction data as heterogeneous information networks(HINs)and conduct knowledge discovery based on the comprehensive structural information and rich semantic information in HINs.Specifically,with the advent of the era of big data,HINs naturally merge heterogeneous data sources,which make it an important way to solve the variety of big data.Therefore,heterogeneous information network analysis has quickly become a hot spot in data mining research and industrial applications.This article provides a comprehensive overview of heterogeneous information network analysis and applications.In addition to the basic concepts in heterogeneous information networks,the focus of this article is on the latest research progress in meta-path based data mining,heterogeneous information networks representation learning,and practical applications of heterogeneous information networks.In the end,this article points out the possible directions of future development.
作者
石川
王睿嘉
王啸
SHI Chuan;WANG Rui-Jia;WANG Xiao(School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China;Beijing Key Laboratory of Intelligent Communications Software and Multimedia,Beijing 100876,China)
出处
《软件学报》
EI
CSCD
北大核心
2022年第2期598-621,共24页
Journal of Software
基金
国家自然科学基金(61772082,61702296,61806020)
国家重点研发计划(2018YFB1402600)。
关键词
异质信息网络
元路径
网络表示学习
图神经网络
heterogeneous information network
meta-path
network representation learning
graph neural network
