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
Jijie ZHANG1, Yan YANG1,2(), Yong LIU1,2
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.Supported by:
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CLC Number:
Jijie ZHANG, Yan YANG, Yong LIU. Adaptive deep graph convolution using initial residual and decoupling operations[J]. Journal of Computer Applications, 2022, 42(1): 9-15.
张继杰, 杨艳, 刘勇. 利用初始残差和解耦操作的自适应深层图卷积[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 9-15.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021071289
符号 | 含义 |
---|---|
节点集 | |
图中节点的集合 | |
图中边的集合 | |
初始的节点特征矩阵 | |
标签矩阵 | |
用于预测的节点表示矩阵 | |
MLP | 多层感知机 |
初始的节点特征维度 | |
图卷积层数 | |
残差保留率 | |
节点类别数 | |
节点 |
Tab. 1 Symbols and their definition
符号 | 含义 |
---|---|
节点集 | |
图中节点的集合 | |
图中边的集合 | |
初始的节点特征矩阵 | |
标签矩阵 | |
用于预测的节点表示矩阵 | |
MLP | 多层感知机 |
初始的节点特征维度 | |
图卷积层数 | |
残差保留率 | |
节点类别数 | |
节点 |
数据集 | 节点数 | 边数 | 训练节点数 | 验证节点数 | 测试节点数 | 节点类别数 | 特征维度 | 边密度 |
---|---|---|---|---|---|---|---|---|
Cora | 2 708 | 5 278 | 140 | 500 | 1 000 | 7 | 1 433 | 0.001 4 |
CiteSeer | 3 327 | 4 552 | 120 | 500 | 1 000 | 6 | 3 703 | 0.000 8 |
PubMed | 19 717 | 44 324 | 60 | 500 | 1 000 | 3 | 500 | 0.000 2 |
Tab. 2 Citation datasets
数据集 | 节点数 | 边数 | 训练节点数 | 验证节点数 | 测试节点数 | 节点类别数 | 特征维度 | 边密度 |
---|---|---|---|---|---|---|---|---|
Cora | 2 708 | 5 278 | 140 | 500 | 1 000 | 7 | 1 433 | 0.001 4 |
CiteSeer | 3 327 | 4 552 | 120 | 500 | 1 000 | 6 | 3 703 | 0.000 8 |
PubMed | 19 717 | 44 324 | 60 | 500 | 1 000 | 3 | 500 | 0.000 2 |
模型 | Cora | CiteSeer | PubMed |
---|---|---|---|
ChebNet | 80.6±1.1 | 70.0±1.3 | 78.1±0.6 |
GCN | 81.3±0.6 | 71.1±0.8 | 78.9±0.5 |
GAT | 83.1±0.4 | 70.8±0.5 | 79.0±0.3 |
SGC | 81.7±0.1 | 71.3±0.3 | 78.9±0.0 |
APPNP | 83.2±0.4 | 71.8±0.5 | 80.2±0.3 |
DAGNN | 84.4±0.5 | 73.3±0.6 | 80.5±0.5 |
ID-AGCN | 84.7±0.6 | 73.4±0.6 | 80.8±0.4 |
Tab. 3 Classification accuracy results for citation datasets
模型 | Cora | CiteSeer | PubMed |
---|---|---|---|
ChebNet | 80.6±1.1 | 70.0±1.3 | 78.1±0.6 |
GCN | 81.3±0.6 | 71.1±0.8 | 78.9±0.5 |
GAT | 83.1±0.4 | 70.8±0.5 | 79.0±0.3 |
SGC | 81.7±0.1 | 71.3±0.3 | 78.9±0.0 |
APPNP | 83.2±0.4 | 71.8±0.5 | 80.2±0.3 |
DAGNN | 84.4±0.5 | 73.3±0.6 | 80.5±0.5 |
ID-AGCN | 84.7±0.6 | 73.4±0.6 | 80.8±0.4 |
数据集 | 残差 保留率 | 图卷积 层数 | weight decay | dropout rate | 学习率 |
---|---|---|---|---|---|
Cora | 0.02 | 15 | 0.005 | 0.85 | 0.01 |
CiteSeer | 0.05 | 10 | 0.020 | 0.55 | 0.01 |
PubMed | 0.01 | 35 | 0.010 | 0.85 | 0.01 |
Tab. 4 Parameter setting
数据集 | 残差 保留率 | 图卷积 层数 | weight decay | dropout rate | 学习率 |
---|---|---|---|---|---|
Cora | 0.02 | 15 | 0.005 | 0.85 | 0.01 |
CiteSeer | 0.05 | 10 | 0.020 | 0.55 | 0.01 |
PubMed | 0.01 | 35 | 0.010 | 0.85 | 0.01 |
模型 | 最优层数 | 分类准确率/% | 运行时间/s |
---|---|---|---|
ChebNet | 3 | 80.6±1.1 | 2.57 |
GCN | 2 | 81.3±0.6 | 1.85 |
GAT | 2 | 83.1±0.4 | 7.38 |
SGC | 3 | 81.7±0.1 | 1.50 |
APPNP | 10 | 83.2±0.4 | 1.61 |
DAGNN | 10 | 84.4±0.5 | 4.31 |
ID-AGCN | 15 | 84.7±0.6 | 4.48 |
Tab. 5 Classification accuracy and running time comparison of different models
模型 | 最优层数 | 分类准确率/% | 运行时间/s |
---|---|---|---|
ChebNet | 3 | 80.6±1.1 | 2.57 |
GCN | 2 | 81.3±0.6 | 1.85 |
GAT | 2 | 83.1±0.4 | 7.38 |
SGC | 3 | 81.7±0.1 | 1.50 |
APPNP | 10 | 83.2±0.4 | 1.61 |
DAGNN | 10 | 84.4±0.5 | 4.31 |
ID-AGCN | 15 | 84.7±0.6 | 4.48 |
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