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

Abstract

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

CLC Number:

TP391

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.

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