基于剪边策略的图残差卷积深层网络模型

毛国君; 王者浩; 黄山; 王翔

doi:10.12263/DZXB.20210152

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基于剪边策略的图残差卷积深层网络模型

学术论文 | 更新时间：2025-12-08

- 基于剪边策略的图残差卷积深层网络模型
- A Deep Residual Graph Convolution Network Based on Dropedge Method
- 电子学报 2022年50卷第9期页码：2205-2214
- 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：
  
  [ "毛国君　男，1966年月出生于内蒙古省赤峰市. 现为福建工程学院计算机与数学学院教授. 在国内外发表学术论文100余篇，知网引用3000以上.E-mail: 19662092@fjut.edu.cn" ]
  [ "王者浩　男，1995年3月出生于福建省福清市，现为福建工程学院在读研究生. 主要研究方向为电气工程人工智能.E-mail: wangzhehaofly@163.com" ]
  [ "黄山　男，1987年5月出生于福建省武夷山市.现为福建工程学院计算机科学与数学学院讲师.研究方向为人工智能与深度学习、固体发光.E-mail: 19872162@fjut.edu.cn" ]
  [ "王翔　男，1992年3月出生于福建省三明市.毕业于北京大学，获博士学位.现为福建工程学院计算机科学与数学学院副教授，硕士生导师.主要研究方向为人工智能，数字信号处理." ]
- 基金信息：
  
  国家自然科学基金(61773415)
- DOI：10.12263/DZXB.20210152
  中图分类号： TP391
- 收稿：2021-01-24，
  
  修回：2021-12-31，
  
  纸质出版：2022-09-25
- 稿件说明：
移动端阅览
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

references

白铂 , 刘玉婷 , 马驰骋 , 等 . 图神经网络 [J]. 中国科学: 数学 , 2020 , 50 ( 3 ): 367 - 384 .

BAI B , LIU Y T , MA C C , et al . Graph neural network [J]. Scientia Sinica (Mathematica) , 2020 , 50 ( 3 ): 367 - 384 . (in Chinese)

钱榕 , 张茹 , 张克君 , 等 . 融合全局和局部特征的胶囊图神经网络 [J]. 吉林大学学报(工学版) , 2021 , 51 ( 3 ): 1048 - 1054 .

QIAN R , ZHANG R , ZHANG K J , et al . Capsule graph neural network based on global and local features fusion [J]. Journal of Jilin University (Engineering and Technology Edition) , 2021 , 51 ( 3 ): 1048 - 1054 . (in Chinese)

高留杰 , 赵文 , 张君福 , 等 . G2S: 基于语义块的知识图谱问答语义解析 [J]. 电子学报 , 2021 , 49 ( 6 ): 1132 - 1141 .

GAO L J , ZHAO W , ZHANG J F , et al . G2S: semantic segment based semantic parsing for question answering over knowledge graph [J]. Acta Electronica Sinica , 2021 , 49 ( 6 ): 1132 - 1141 . (in Chinese)

HAMILTON W L , YING R , LESKOVEC J . Inductive representation learning on large graphs [C]// NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems . Red Hook, NY : Curran Associates Inc , 2017 : 1025 - 1035 .

WANG X L , YE Y F , GUPTA A . Zero-shot recognition via semantic embeddings and knowledge graphs [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 6857 - 6866 .

LIN W Y , JI S X , LI B C . Adversarial attacks on link prediction algorithms based on graph neural networks [C]// ASIA CCS'20: Proceedings of the 15th ACM Asia Conference on Computer and Communications Security . New York : ACM , 2020 : 370 - 380 .

车向北 , 康文倩 , 邓彬 , 等 . 一种基于图神经网络的SDN路由性能预测模型 [J]. 电子学报 , 2021 , 49 ( 3 ): 484 - 491 .

CHE X B , KANG W Q , DENG B , et al . A prediction model of SDN routing performance based on graph neural network [J]. Acta Electronica Sinica , 2021 , 49 ( 3 ): 484 - 491 . (in Chinese)

ZITNIK M , AGRAWAL M , LESKOVEC J . Modeling polypharmacy side effects with graph convolutional networks [J]. Bioinformatics(Oxford, England) , 2018 , 34 ( 13 ): i457 - i466 .

PEROZZI B , AL-RFOU R , SKIENA S . DeepWalk: online learning of social representations [C]// KDD'14: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM , 2014 : 701 - 710 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [EB/OL]. ( 2016-09-09 ). https://arxiv.org/abs/1609.02907 https://arxiv.org/abs/1609.02907 .

KARPATHY A , TODERICI G , SHETTY S , et al . Large-scale video classification with convolutional neural networks [C]// CVPR'14: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus : IEEE , 2014 : 1725 - 1732 .

SHELHAMER E , LONG J , DARRELL T . Fully convolutional networks for semantic segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. Piscataway: IEEE , 2016 , 39 ( 4 ): 640 - 651 .

CHEN M , WEI Z , HUANG Z , et al . Simple and deep graph convolutional networks [C]// International Conference on Machine Learning . Vienna : ICML , 2020 : 1725 - 1735 .

XU K , LI C , TIAN Y , et al . Representation learning on graphs with jumping knowledge networks [C]// International Conference on Machine Learning . Stockholm : ICML , 2018 : 5453 - 5462 .

LI G H , MÜLLER M , THABET A , et al . DeepGCNs: can GCNs go as deep as CNNs? [C]// 2019 IEEE/CVF International Conference on Computer Vision(ICCV) . Piscataway : IEEE , 2019 : 9266 - 9275 .

WU H B , GU X D . Towards dropout training for convolutional neural networks [J]. Neural Networks , 2015 , 71 : 1 - 10 .

DO T H , NGUYEN D M , BEKOULIS G , et al . Graph convolutional neural networks with node transition probability-based message passing and DropNode regularization [J]. Expert Systems With Applications , 2021 , 174 : 114711 .

RONG Y , HUANG W B , XU T Y , et al . DropEdge: towards deep graph convolutional networks on node classification [EB/OL]. ( 2019-07-25 ). https://arxiv.org/abs/ 1907. 10903 https://arxiv.org/abs/1907.10903 .

SRIVASTAVA N , HINTON G E , KRIZHEVSKY A , et al . Dropout: a simple way to prevent neural networks from overfitting [J]. Journal of Machine Learning Research , 2014 , 15 ( 1 ): 1929 - 1958 .

SCARSELLI F , GORI M , TSOI A C , et al . The graph neural network model [J]. IEEE Transactions on Neural Networks , 2009 , 20 ( 1 ): 61 - 80 .

BRUNA J , ZAREMBA W , SZLAM A , et al . Spectral networks and locally connected networks on graphs [EB/OL].( 2013-12-21 ). https://arxiv.org/abs/ 1312.6 203v3 https://arxiv.org/abs/1312.6203v3 .

郭嘉琰 , 李荣华 , 张岩 , 等 . 基于图神经网络的动态网络异常检测算法 [J]. 软件学报 , 2020 , 31 ( 3 ): 748 - 762 .

GUO J Y , LI R H , ZHANG Y , et al . Graph neural network based anomaly detection in dynamic networks [J]. Journal of Software , 2020 , 31 ( 3 ): 748 - 762 . (in Chinese)

WANG R , HUANG C Y , WANG X Y . Global relation reasoning graph convolutional networks for human pose estimation [J]. IEEE Access , 2020 , 8 : 38472 - 38480 .

YU K , JIANG H , LI T , et al . Data fusion oriented graph convolution network model for rumor detection [J]. IEEE Transactions on Network and Service Management , 2020 , 17 ( 4 ): 2171 - 2181 .

HE K M , ZHANG X Y , REN S Q , et al . Identity Mappings in Deep Residual Networks [C]// European Conference on Computer Vision . Cham : Springer , 2016 : 630 - 645 .

OONO K , SUZUKI T . Graph neural networks exponentially lose expressive power for node classification [EB/OL]. ( 2019-05-27 ). https://arxiv.org/abs/ 1905.10947 https://arxiv.org/abs/1905.10947 .

SEN P , NAMATA G , BILGIC M , et al . Collective classification in network data [J]. AI Magazine , 2008 , 29 ( 3 ): 93 .

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