基于深度学习的异质信息网络表示学习方法综述

Deep Learning-based Heterogeneous Information Network Representation:A Survey

万物依存而在,现实世界中的实体之间存在着各种不同的关联关系,如人与人之间的关系可以构成社交网络,学者通过共同发表论文、引用文献构成引文网络。同质网络将节点和边抽象为单一类型,但是这会造成大量的信息丢失。为了更大程度地保证信息的完整性和丰富性,有研究者提出了异质信息网络的概念,即包含多种类型节点和边的网络模式。将异质信息网络中的拓扑结构和语义信息嵌入到低维向量空间中,下游任务能够利用异质信息网络中的丰富信息进行机器学习或数据挖掘任务。文中总结了近年来基于深度学习模型的异质信息网络表示学习方法的研究成果,同时聚焦两类关键问题——异质信息网络语义自动提取和动态异质信息网络的表示学习方法,列举了异质信息网络表示学习新的应用场景,并展望了异质信息网络的未来发展趋势。

Things in the nature connect mutually.There are various associations between them in the real world.For example,social networks can be constructed by the user-user relationships.The article-author relationship can be used to construct a citation network.In homogeneous networks,nodes or edges are all in the same type,resulting in a lot of information loss.In order to ensure the integrity and richness of information to a greater extent,researchers have proposed heterogeneous information network(HIN),a network model containing multiple types of nodes or edges.By embedding the topological structure and semantic information of HIN into a low-dimensional vector space,downstream tasks can utilize the rich information in the HIN for machine learning or data mining.This paperfocuses on the HIN-based representation learning tasks,and summarizes the recent representation learning methods of HIN which are based on deep learning models.We focus on two main issues:semantics extraction of HIN and information preserving of dynamic HIN.We also illustrate some new applications of HIN-based representation learning,and propose the future development trend of heterogeneous information networks.