A Deep Residual Graph Convolution Network Based on Dropedge Method

MAO Guo-jun; WANG Zhe-hao; HUANG Shan; WANG Xiang

doi:10.12263/DZXB.20210152

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A Deep Residual Graph Convolution Network Based on Dropedge Method

PAPERS | 更新时间：2025-12-08

- A Deep Residual Graph Convolution Network Based on Dropedge Method
- ACTA ELECTRONICA SINICA Vol. 50, Issue 9, Pages: 2205-2214(2022)
- 作者机构：
  
  福建工程学院福建省大数据挖掘与应用重点实验室, 福建福州 350118
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210152
  CLC： TP391
- Received：24 January 2021，
  
  Revised：2021-12-31，
  
  Published：25 September 2022
- 稿件说明：
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毛国君,王者浩,黄山等.基于剪边策略的图残差卷积深层网络模型[J].电子学报,2022,50(09):2205-2214.

MAO Guo-jun,WANG Zhe-hao,HUANG Shan,et al.A Deep Residual Graph Convolution Network Based on Dropedge Method[J].ACTA ELECTRONICA SINICA,2022,50(09):2205-2214.
毛国君,王者浩,黄山等.基于剪边策略的图残差卷积深层网络模型[J].电子学报,2022,50(09):2205-2214. DOI： 10.12263/DZXB.20210152.

MAO Guo-jun,WANG Zhe-hao,HUANG Shan,et al.A Deep Residual Graph Convolution Network Based on Dropedge Method[J].ACTA ELECTRONICA SINICA,2022,50(09):2205-2214. DOI： 10.12263/DZXB.20210152.

摘要

图神经网络自2005年以来已经逐步成为图学习中的一个重要的研究分支，其中最为活跃的是图卷积神经网络.由于图数据在现实世界中广泛存在，因此有效地完成图结构数据的学习具有很大的应用前景.目前出现的大多数图卷积神经网络模型基本都是浅层结构，过平滑问题成为制约该领域发展的瓶颈问题.本文提出了一种称为dri-GCN（Graph Convolutional Network via dropedge， residual and identity mapping）的图残差卷积深层网络模型，该模型集成了图剪边、初始残差和恒等映射技术.主要思想包括：利用图剪边技术增加学习数据的多样性，以防止学习过程中的过拟合现象；构建恒等映射下的初始残差网络，来扩展残差单元的学习路径，以削弱学习过程中的过平滑问题.实验结果表明，本文提出的dri-GCN模型可以帮助构建深层图卷积神经网络，通过网络层次的加深可以获得优于浅层网络的学习准确率.

Abstract

Graph neural network (GNN) has become an important research field since 2005

and the most active branch is the graph convolutional network(GCN). In the real world

many applications are directly related to graphic data

so learning from graphic structure data is becoming more and more important

and can has a huge application prospect. However

most of the existing graph convolution neural network models can only support very few convoluted layers. This is mainly because of the over smoothing problem in GCNs

which has become one of the main bottlenecks in GCNs. In this paper

a novel GCN network: dri-GCN is proposed

which integrates the technologies of the Initial Residual

Identity Mapping and DropEdge into the traditional GCN. The main contributions include: using DropEdge to increase data diversity and prevent over-fitting; constructing the residual convolutional network under the constant mapping to extend the learning paths

which can effectively weaken the over smooth problem in GCNs. Experimental results show that dri-GCN model can help building the deeper graph convolution neural networks. There is no doubt that deeper GCNs can achieve better learning accuracy than shallower networks.

关键词

Keywords

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