图卷积神经网络综述  

Survey on graph convolutional neural network

作　　者：刘俊奇 涂文轩 祝恩[1] LIU Jun-qi;TU Wen-xuan;ZHU En(College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学计算机学院,湖南长沙410073

出　　处：《计算机工程与科学》2023年第8期1472-1481,共10页Computer Engineering & Science

摘　　要：由于图数据的广泛存在,图卷积神经网络发展速度越来越快。根据卷积算子定义方式的不同,图卷积神经网络大体可以分为2类,其中一类基于谱方法,另一类基于空间方法。首先对这2类方法中的代表性模型以及二者之间的联系进行详细论述,并进一步全面总结图的池化操作;接着介绍了图卷积神经网络在各个领域中的广泛应用;最后提出了图卷积神经网络的几个可能的发展方向并对全文进行了总结。With the widespread existence of graph data,the development of graph convolutional neural networks(GCNNs)is becoming faster and faster.According to the different definitions of the convolution operator,GCNNs can be roughly divided into two categories:one based on spectral methods and the other based on spatial methods.Firstly,representative models of these two categories and their connections are discussed in detail,and then the graph pooling operations are comprehensively summarized.Furthermore,the extensive applications of GCNNs in various fields are introduced,and several possible development directions of GCNNs are proposed.Finally,a conclusion is done.

关 键 词：图数据 卷积算子 图卷积神经网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]