基于图卷积神经网络的节点分类方法研究综述

doi:10.11896/jsjkx.230600071

摘要/Abstract

摘要： 节点分类任务是图领域中的重要研究工作之一。近年来随着图卷积神经网络研究工作的不断深入,基于图卷积神经网络的节点分类研究及其应用都取得了重大进展。图卷积神经网络是基于卷积发展出的一类图神经网络,能处理图数据且具有卷积神经网络的优点,已成为图节点分类方法中最活跃的一个研究分支。对基于图卷积神经网络的节点分类方法的研究进展进行综述,首先介绍图的相关概念、节点分类的任务定义和常用的图数据集;然后探讨两类经典图卷积神经网络——谱域和空间域图卷积神经网络,以及图卷积神经网络在节点分类领域面临的挑战;之后从模型和数据两个视角分析图卷积神经网络在节点分类任务中的研究成果和未解决的问题;最后对基于图卷积神经网络的节点分类研究方向进行展望,并总结全文。

关键词: 图数据, 节点分类, 图神经网络, 图卷积神经网络

Abstract: Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node classification based on graph convolutional neural networks.Graph convolutional neural networks are kind of graph neural network method based on convolution.It can handle graph data and have the advantages of convolutional neural networks,and have become the most active branch of graph node classification research.This paper first introduces the related concepts of graph,the definition of node classification and commonly used graph datasets.Then,it reviews two classic graph convolutional neural networks,spectral domain and spatial domain graph convolutional neural networks,and discusses the challenges of using graph con-volutional neural networks to study node classification.Next,it analyzes the research progress and unresolved issues of graph convolutional neural networks in node classification tasks from the perspectives of model and data.Finally,this paper gives insights into the research direction on node classification based on graph convolutional neural networks.

Key words: Graph structure data, Node classification, Graph neural network, Graph convolutional neural network

中图分类号:

TP391

张丽英, 孙海航, 孙玉发, 石兵波. 基于图卷积神经网络的节点分类方法研究综述[J]. 计算机科学, 2024, 51(4): 95-105. https://doi.org/10.11896/jsjkx.230600071

ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo. Review of Node Classification Methods Based on Graph Convolutional Neural Networks[J]. Computer Science, 2024, 51(4): 95-105. https://doi.org/10.11896/jsjkx.230600071

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