Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 9-15.DOI: 10.11772/j.issn.1001-9081.2021071289

• Artificial intelligence • Previous Articles     Next Articles

Adaptive deep graph convolution using initial residual and decoupling operations

Jijie ZHANG1, Yan YANG1,2(), Yong LIU1,2   

  1. 1.School of Computer Science and Technology,Heilongjiang University,Harbin Heilongjiang 150080,China
    2.Key Laboratory of Database and Parallel Computing of Heilongjiang Province (Heilongjiang University),Harbin Heilongjiang 150080,China
  • Received:2021-07-19 Revised:2021-08-13 Accepted:2021-08-19 Online:2021-08-13 Published:2022-01-10
  • Contact: Yan YANG
  • About author:ZHANG Jijie, born in 1998, M. S. candidate. His research interests include graph representation learning, recommendation system.
    YANG Yan, born in 1973, Ph. D., professor. Her research interests include data mining, social network.
    LIU Yong, born in 1975, Ph. D., associate professor. His research interests include data mining, machine learning.
  • Supported by:
    Natural Science Foundation of Heilongjiang Province(LH2020F043)


张继杰1, 杨艳1,2(), 刘勇1,2   

  1. 1.黑龙江大学 计算机科学技术学院,哈尔滨 150080
    2.黑龙江省数据库与并行计算重点实验室(黑龙江大学),哈尔滨 150080
  • 通讯作者: 杨艳
  • 作者简介:张继杰(1998—),男,山东青岛人,硕士研究生,CCF会员,主要研究方向:图表示学习、推荐系统


The traditional Graph Convolutional Network (GCN) and many of its variants achieve the best effect in the shallow layers, and do not make full use of higher-order neighbor information of nodes in the graph. The subsequent deep graph convolution models can solve the above problem, but inevitably generate the problem of over-smoothing, which makes the models impossible to effectively distinguish different types of nodes in the graph. To address this problem, an adaptive deep graph convolution model using initial residual and decoupling operations, named ID-AGCN (model using Initial residual and Decoupled Adaptive Graph Convolutional Network), was proposed. Firstly, the node’s representation transformation as well as feature propagation was decoupled. Then, the initial residual was added to the node’s feature propagation process. Finally, the node representations obtained from different propagation layers were combined adaptively, appropriate local and global information was selected for each node to obtain node representations containing rich information, and a small number of labeled nodes were used for supervised training to generate the final node representations. Experimental result on three datasets Cora, CiteSeer and PubMed indicate that the classification accuracy of ID-AGCN is improved by about 3.4 percentage points, 2.3 percentage points and 1.9 percentage points respectively, compared with GCN. The proposed model has superiority in alleviating over-smoothing.

Key words: node classification, initial residual, decoupling, adaptive, Graph Convolutional Network (GCN)



关键词: 节点分类, 初始残差, 解耦, 自适应, 图卷积网络

CLC Number: