Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 663-670.DOI: 10.11772/j.issn.1001-9081.2023030353
Special Issue: 人工智能
• Artificial intelligence • Next Articles
Dapeng XU1(), Xinmin HOU1,2
Received:
2023-04-03
Revised:
2023-05-08
Accepted:
2023-05-09
Online:
2023-05-30
Published:
2024-03-10
Contact:
Dapeng XU
About author:
HOU Xinmin, born in 1972, Ph. D., professor. His research interests include graph theory and its applications, complex network, graph neural network.
Supported by:
通讯作者:
徐大鹏
作者简介:
侯新民(1972—),男,山东郓城人,教授,博士,主要研究方向:图论及其应用、复杂网络、图神经网络。
基金资助:
CLC Number:
Dapeng XU, Xinmin HOU. Feature selection method for graph neural network based on network architecture design[J]. Journal of Computer Applications, 2024, 44(3): 663-670.
徐大鹏, 侯新民. 基于网络结构设计的图神经网络特征选择方法[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 663-670.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030353
数据集 | 节点数 | 边数 | 特征维数 | 标签数 | 训练集节点数 | 验证集节点数 | 测试集节点数 | 同质比 |
---|---|---|---|---|---|---|---|---|
Cora | 2 708 | 5 429 | 1 433 | 7 | 140 | 500 | 1 000 | 0.81 |
Citeseer | 3 327 | 4 732 | 3 703 | 6 | 120 | 500 | 1 000 | 0.74 |
Pubmed | 19 717 | 44 338 | 500 | 3 | 60 | 500 | 1 000 | 0.80 |
Cornell | 183 | 295 | 1 703 | 5 | 87 | 59 | 37 | 0.30 |
Texas | 183 | 309 | 1 703 | 5 | 87 | 59 | 37 | 0.11 |
Wisconsin | 251 | 499 | 1 703 | 5 | 120 | 80 | 51 | 0.21 |
Tab. 1 Statistics of experimental datasets
数据集 | 节点数 | 边数 | 特征维数 | 标签数 | 训练集节点数 | 验证集节点数 | 测试集节点数 | 同质比 |
---|---|---|---|---|---|---|---|---|
Cora | 2 708 | 5 429 | 1 433 | 7 | 140 | 500 | 1 000 | 0.81 |
Citeseer | 3 327 | 4 732 | 3 703 | 6 | 120 | 500 | 1 000 | 0.74 |
Pubmed | 19 717 | 44 338 | 500 | 3 | 60 | 500 | 1 000 | 0.80 |
Cornell | 183 | 295 | 1 703 | 5 | 87 | 59 | 37 | 0.30 |
Texas | 183 | 309 | 1 703 | 5 | 87 | 59 | 37 | 0.11 |
Wisconsin | 251 | 499 | 1 703 | 5 | 120 | 80 | 51 | 0.21 |
模型 | Cora | Citeseer | Pubmed | Cornell | Texas | Wisconsin |
---|---|---|---|---|---|---|
GCN | 81.50 | 70.30 | 79.00 | 58.65 | 61.35 | 54.71 |
DGI | 82.50 | 71.60 | 78.40 | 57.70 | 59.70 | 54.80 |
GCNII | 85.50 | 73.40 | 80.30 | 74.86 | 69.46 | 74.12 |
SEP-N | 84.80 | 72.90 | 80.20 | 57.40 | 60.60 | 61.20 |
GAT | 83.00 | 72.50 | 79.00 | 58.92 | 58.38 | 55.29 |
GATv2 | 82.30 | 72.20 | 78.50 | 57.84 | 61.35 | 54.90 |
FSGAT | 84.40 | 73.10 | 80.50 | 60.00 | 63.51 | 56.67 |
FSGATv2 | 83.20 | 73.00 | 80.70 | 59.46 | 64.60 | 56.08 |
Tab. 2 Statistics of node classification accuracy for different models
模型 | Cora | Citeseer | Pubmed | Cornell | Texas | Wisconsin |
---|---|---|---|---|---|---|
GCN | 81.50 | 70.30 | 79.00 | 58.65 | 61.35 | 54.71 |
DGI | 82.50 | 71.60 | 78.40 | 57.70 | 59.70 | 54.80 |
GCNII | 85.50 | 73.40 | 80.30 | 74.86 | 69.46 | 74.12 |
SEP-N | 84.80 | 72.90 | 80.20 | 57.40 | 60.60 | 61.20 |
GAT | 83.00 | 72.50 | 79.00 | 58.92 | 58.38 | 55.29 |
GATv2 | 82.30 | 72.20 | 78.50 | 57.84 | 61.35 | 54.90 |
FSGAT | 84.40 | 73.10 | 80.50 | 60.00 | 63.51 | 56.67 |
FSGATv2 | 83.20 | 73.00 | 80.70 | 59.46 | 64.60 | 56.08 |
实际标签 | 预测标签 | |||
---|---|---|---|---|
0 | 1 | 2 | 合计 | |
总计 | 190 | 420 | 390 | 1 000 |
0 | 140 | 16 | 24 | 180 |
1 | 22 | 345 | 46 | 413 |
2 | 28 | 59 | 320 | 407 |
Tab. 3 Classification confusion matrix of dataset Pubmed under model FSGAT
实际标签 | 预测标签 | |||
---|---|---|---|---|
0 | 1 | 2 | 合计 | |
总计 | 190 | 420 | 390 | 1 000 |
0 | 140 | 16 | 24 | 180 |
1 | 22 | 345 | 46 | 413 |
2 | 28 | 59 | 320 | 407 |
实际标签 | 预测标签 | |||
---|---|---|---|---|
0 | 1 | 2 | 合计 | |
总计 | 197 | 421 | 382 | 1 000 |
0 | 150 | 15 | 15 | 180 |
1 | 23 | 340 | 50 | 413 |
2 | 24 | 66 | 317 | 407 |
Tab. 4 Classification confusion matrix of dataset Pubmed under model FSGATv2
实际标签 | 预测标签 | |||
---|---|---|---|---|
0 | 1 | 2 | 合计 | |
总计 | 197 | 421 | 382 | 1 000 |
0 | 150 | 15 | 15 | 180 |
1 | 23 | 340 | 50 | 413 |
2 | 24 | 66 | 317 | 407 |
算法 | Cora | Citeseer | Pubmed | Cornell | Texas | Wisconsin |
---|---|---|---|---|---|---|
GAT_map | 83.30 | 79.30 | 61.62 | 56.27 | ||
GAT_fs | 83.60 | 72.80 | 79.40 | 63.24 | 55.49 | |
GATv2_map | 72.30 | 79.20 | 58.65 | 63.51 | 55.29 | |
GATv2_fs | 82.30 | 79.30 | 59.19 | 62.70 |
Tab. 5 Statistics of node classification accuracy in ablation experiments
算法 | Cora | Citeseer | Pubmed | Cornell | Texas | Wisconsin |
---|---|---|---|---|---|---|
GAT_map | 83.30 | 79.30 | 61.62 | 56.27 | ||
GAT_fs | 83.60 | 72.80 | 79.40 | 63.24 | 55.49 | |
GATv2_map | 72.30 | 79.20 | 58.65 | 63.51 | 55.29 | |
GATv2_fs | 82.30 | 79.30 | 59.19 | 62.70 |
数据集 | d | 不同模型的节点分类准确率/% | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
GAT_1 | GAT_2 | GAT | GATv2_1 | GATv2_2 | GATv2 | GCN_1 | GCN_2 | GCN | ||||
Cora | 92 | 100 | 1 433 | 82.10 | 81.10 | 83.00 | 79.50 | 80.30 | 82.30 | 79.50 | 79.10 | 81.50 |
Citeseer | 157 | 146 | 3 703 | 72.00 | 70.80 | 72.50 | 70.08 | 71.90 | 72.20 | 68.30 | 68.10 | 70.30 |
Pubmed | 51 | 46 | 500 | 78.50 | 78.20 | 79.00 | 79.40 | 79.60 | 78.50 | 78.10 | 78.10 | 79.00 |
Cornell | 219 | 182 | 1 703 | 55.95 | 54.05 | 58.92 | 58.37 | 58.37 | 57.84 | 58.64 | 59.20 | 58.65 |
Texas | 129 | 158 | 1 703 | 58.91 | 59.46 | 58.38 | 59.45 | 60.80 | 61.35 | 61.08 | 60.81 | 61.35 |
Wisconsin | 137 | 128 | 1 703 | 55.29 | 53.29 | 55.29 | 54.90 | 55.88 | 54.90 | 53.73 | 54.90 | 54.71 |
Tab. 6 Statistics of featuresub’s node classification accuracy on GAT, GATv2 and GCN
数据集 | d | 不同模型的节点分类准确率/% | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
GAT_1 | GAT_2 | GAT | GATv2_1 | GATv2_2 | GATv2 | GCN_1 | GCN_2 | GCN | ||||
Cora | 92 | 100 | 1 433 | 82.10 | 81.10 | 83.00 | 79.50 | 80.30 | 82.30 | 79.50 | 79.10 | 81.50 |
Citeseer | 157 | 146 | 3 703 | 72.00 | 70.80 | 72.50 | 70.08 | 71.90 | 72.20 | 68.30 | 68.10 | 70.30 |
Pubmed | 51 | 46 | 500 | 78.50 | 78.20 | 79.00 | 79.40 | 79.60 | 78.50 | 78.10 | 78.10 | 79.00 |
Cornell | 219 | 182 | 1 703 | 55.95 | 54.05 | 58.92 | 58.37 | 58.37 | 57.84 | 58.64 | 59.20 | 58.65 |
Texas | 129 | 158 | 1 703 | 58.91 | 59.46 | 58.38 | 59.45 | 60.80 | 61.35 | 61.08 | 60.81 | 61.35 |
Wisconsin | 137 | 128 | 1 703 | 55.29 | 53.29 | 55.29 | 54.90 | 55.88 | 54.90 | 53.73 | 54.90 | 54.71 |
数据集 | 选择的特征数量 | 准确率/% |
---|---|---|
Cora | 225 | 68.20 |
Citeseer | 450 | 57.70 |
Pubmed | 105 | 66.80 |
Cornell | 255 | 48.85 |
Texas | 255 | 50.10 |
Wisconsin | 255 | 43.20 |
Tab. 7 Statistics of node classification accuracy in literature [26]
数据集 | 选择的特征数量 | 准确率/% |
---|---|---|
Cora | 225 | 68.20 |
Citeseer | 450 | 57.70 |
Pubmed | 105 | 66.80 |
Cornell | 255 | 48.85 |
Texas | 255 | 50.10 |
Wisconsin | 255 | 43.20 |
1 | 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. 10.1109/tnn.2008.2005605 |
2 | SCARSELLI F, TSOI A C, GORI M, et al. Graphical-based learning environments for pattern recognition [C]// Proceedings of the 2004 Structural, Syntactic, and Statistical Pattern Recognition. Cham: Springer, 2004: 42-56. 10.1007/978-3-540-27868-9_4 |
3 | ZHOU F, CAO C, ZHANG K, et al. Meta-GNN: on few-shot node classification in graph meta-learning [C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 2357-2360. 10.1145/3357384.3358106 |
4 | HANG M, NEVILLE J, RIBEIRO B. A collective learning framework to boost GNN expressiveness for node classification [EB/OL]. [2023-01-11]. . |
5 | CHEN M, WEI Z, HUANG Z, et al. Simple and deep graph convolutional networks [C]// Proceedings of the 37th International Conference on Machine Learning. New York: JMLR.org, 2020: 1725-1735. |
6 | ZHAO T, ZHANG X, WANG S. GraphSMOTE: imbalanced node classification on graphs with graph neural networks [C]// Proceedings of the 14th ACM International Conference on Web Search and Data Mining. New York: ACM, 2021: 833-841. 10.1145/3437963.3441720 |
7 | WU W, LI B, LUO C, et al. Hashing-accelerated graph neural networks for link prediction [C]// Proceedings of the 2021 Web Conference. New York: ACM, 2021: 2910-2920. 10.1145/3442381.3449884 |
8 | YING R, HE R, CHEN K, et al. Graph convolutional neural networks for web-scale recommender systems [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2018: 974-983. 10.1145/3219819.3219890 |
9 | ZHANG M, CHEN Y. Link prediction based on graph neural networks [C]// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2018: 5171-5181. |
10 | WU B, YANG X, PAN S, et al. Adapting membership inference attacks to GNN for graph classification: approaches and implications [C]// Proceedings of the 2021 IEEE International Conference on Data Mining. Piscataway: IEEE, 2021: 1421-1426. 10.1109/icdm51629.2021.00182 |
11 | LE T, BERTOLINI M, NOÉ F, et al. Parameterized hypercomplex graph neural networks for graph classification [C]// Proceedings of the 30th International Conference on Artificial Neural Networks. Berlin: Springer, 2021: 204-216. 10.1007/978-3-030-86365-4_17 |
12 | MA H, BIAN Y, RONG Y, et al. Multi-view graph neural networks for molecular property prediction [EB/OL]. (2020-06-12) [2022-04-21]. . 10.1093/bioinformatics/btac039 |
13 | MENG Y, ZONG S, LI X, et al. GNN-LM: language modeling based on global contexts via GNN [EB/OL]. (2022-05-04) [2022-10-11]. . |
14 | MAURYA S K, LIU X, MURATA T. Graph neural networks for fast node ranking approximation [J]. ACM Transactions on Knowledge Discovery from Data, 2021, 15(5): 78. 10.1145/3446217 |
15 | WU S, SUN F, ZHANG W, et al. Graph neural networks in recommender systems: a survey [J]. ACM Computing Surveys, 2022, 55(5): 97. 10.1145/3535101 |
16 | ZHANG H, LU G, ZHAN M, et al. Semi-supervised classification of graph convolutional networks with Laplacian rank constraints [J]. Neural Processing Letters, 2022, 54: 2645-2656. 10.1007/s11063-020-10404-7 |
17 | RUIZ L, GAMA F, RIBEIRO A. Gated graph recurrent neural networks [J]. IEEE Transactions on Signal Processing, 2020, 68: 6303-6318. 10.1109/tsp.2020.3033962 |
18 | HAMILTON W L, YING Z, LESKOVEC J. Inductive representation learning on large graphs [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 1025-1035. 10.7551/mitpress/11474.003.0014 |
19 | VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks [EB/OL]. [2021-06-02]. . |
20 | BRODY S, ALON U, YAHAV E. How attentive are graph attention networks? [EB/OL]. [2022-09-05]. . |
21 | BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate [EB/OL]. (2016-05-19) [2021-03-16]. . 10.1017/9781108608480.003 |
22 | WU J, CHEN X, XU K, et al. Structural entropy guided graph hierarchical pooling [C]// Proceedings of the 39th International Conference on Machine Learning. New York: JMLR.org, 2022: 24017-24030. |
23 | CHENG H, ZHOU J T, TAY W P, et al. Attentive graph neural networks for few-shot learning [C]// Proceedings of the 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval. Piscataway: IEEE, 2022: 152-157. 10.1109/mipr54900.2022.00033 |
24 | FAN W, LIU K, LIU H, et al. AutoFS: automated feature selection via diversity-aware interactive reinforcement learning[C]// Proceedings of the 2020 IEEE International Conference on Data Mining. Piscataway: IEEE, 2020: 1008-1013. 10.1109/icdm50108.2020.00117 |
25 | ZHAO X, LIU K, FAN W, et al. Simplifying reinforced feature selection via restructured choice strategy of single agent [C]// Proceedings of the 2020 IEEE International Conference on Data Mining. Piscataway: IEEE, 2020: 871-880. 10.1109/icdm50108.2020.00096 |
26 | ACHARYA D B, ZHANG H. Feature selection and extraction for graph neural networks [C]// Proceedings of the 2020 ACM Southeast Conference. New York: ACM, 2020: 252-255. 10.1145/3374135.3385309 |
27 | ABID A, BALIN M F, ZOU J. Concrete autoencoders for differentiable feature selection and reconstruction [EB/OL]. (2019-01-31) [2022-04-25]. . |
28 | BAMA S S, SARAVANAN A. Efficient classification using average weighted pattern score with attribute rank based feature selection [J]. International Journal of Intelligent Systems and Applications, 2019, 11(7): 29-42. 10.5815/ijisa.2019.07.04 |
29 | SREEJA N K, SANKAR A. Pattern matching based classification using ant colony optimization based feature selection [J]. Applied Soft Computing, 2015, 31: 91-102. 10.1016/j.asoc.2015.02.036 |
30 | XIONG S, LIU R, YI C. Graph-AutoFS: auto feature selection in graph neural [C]// Proceedings of the 7th International Conference on Computing and Data Engineering. New York: ACM, 2021: 41-46. 10.1145/3456172.3456191 |
31 | MAURYA S K, LIU X, MURATA T. Improving graph neural networks with simple architecture design [EB/OL]. (2021-05-17) [2021-11-15]. . 10.1145/3446217 |
32 | WANG Y, ZHAO X, XU T, et al. AutoField: automating feature selection in deep recommender systems [C]// Proceedings of the 2022 ACM Web Conference. New York: ACM, 2022: 1977-1986. 10.1145/3485447.3512071 |
33 | ZHU J, YAN Y, ZHAO L, et al. Beyond homophily in graph neural networks: current limitations and effective designs [C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2020: 7793-7804. |
34 | SEN P, NAMATA G, BILGIC M, et al. Collective classification in network data[J]. AI Magazine, 2008, 29(3): 93-107. 10.1609/aimag.v29i3.2157 |
35 | PEI H, WEI B, CHANG K C C, et al. Geom-GCN: geometric graph convolutional networks [EB/OL]. (2020-02-14) [2021-03-05]. . |
36 | KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL]. (2017-02-22) [2021-05-27]. . 10.48550/arXiv.1609.02907 |
37 | VELICKOVIĆ P, FEDUS W, HAMILTON W L, et al. Deep graph infomax[C/OL]// Proceedings of the 2019 International Conference for Learning Representations. [2023-03-01]. . |
[1] | Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877. |
[2] | Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718. |
[3] | Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703. |
[4] | Yunchuan HUANG, Yongquan JIANG, Juntao HUANG, Yan YANG. Molecular toxicity prediction based on meta graph isomorphism network [J]. Journal of Computer Applications, 2024, 44(9): 2964-2969. |
[5] | Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974. |
[6] | Xiyuan WANG, Zhancheng ZHANG, Shaokang XU, Baocheng ZHANG, Xiaoqing LUO, Fuyuan HU. Unsupervised cross-domain transfer network for 3D/2D registration in surgical navigation [J]. Journal of Computer Applications, 2024, 44(9): 2911-2918. |
[7] | Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499. |
[8] | Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557. |
[9] | Yanjie GU, Yingjun ZHANG, Xiaoqian LIU, Wei ZHOU, Wei SUN. Traffic flow forecasting via spatial-temporal multi-graph fusion [J]. Journal of Computer Applications, 2024, 44(8): 2618-2625. |
[10] | Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction [J]. Journal of Computer Applications, 2024, 44(8): 2643-2650. |
[11] | Zheng WU, Zhiyou CHENG, Zhentian WANG, Chuanjian WANG, Sheng WANG, Hui XU. Deep learning-based classification of head movement amplitude during patient anaesthesia resuscitation [J]. Journal of Computer Applications, 2024, 44(7): 2258-2263. |
[12] | Huanhuan LI, Tianqiang HUANG, Xuemei DING, Haifeng LUO, Liqing HUANG. Public traffic demand prediction based on multi-scale spatial-temporal graph convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2065-2072. |
[13] | Zhi ZHANG, Xin LI, Naifu YE, Kaixi HU. DKP: defending against model stealing attacks based on dark knowledge protection [J]. Journal of Computer Applications, 2024, 44(7): 2080-2086. |
[14] | Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks [J]. Journal of Computer Applications, 2024, 44(7): 2310-2318. |
[15] | Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||