Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (4): 224-234.doi: 10.11896/jsjkx.250600033

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-channel Graph Kolmogorov-Arnold Network Based on WL Graph Core

WANG Jinghong1,2,3,4, LI Pengchao1,3,4,5, MI Jusheng4,5,6, WANG Wei1,4,5   

  1. 1 College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
    2 College of Artificial Intelligence, Hebei University of Engineering Technology, Shijiazhuang 050020, China
    3 State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei 230088, China
    4 Hebei Provincial Key Laboratory of Network and Information Security, Shijiazhuang 050024, China
    5 Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Shijiazhuang 050024, China
    6 School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, China
  • Received:2025-06-06 Revised:2025-08-28 Online:2026-04-15 Published:2026-04-08
  • About author:WANG Jinghong,born in 1967.Ph.D,professor,is a member of CCF(No.58341S).Her main research interests include artificial intelligence,pattern recognition,machine learning and data mining.
  • Supported by:
    Natural Science Foundation of Hebei Province(F2024205028),Postgraduate’s Innovation Fund Project of Hebei Province(CXZZSS2025049),Hebei Normal University Science and Technology Research Fund(L2023J05,L2024C05),Key Development Fund of Hebei Normal University(L2024ZD06) and Opening Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK(COGOS-2025HE07).

PDF (PC)

69

Abstract: As an emerging deep learning method,graph neural networks have demonstrated powerful capabilities in modeling and representing graph structure data in various graph learning tasks.However,most existing graph neural networks focus on single-channel graph convolution and fail to make full use of the rich and diverse relationship information in real-world graph data.To deeply mine multi-relational features in graph data and enhance the modeling capabilities of graph neural networks,this paper proposes a multi-channel graph Kolmogorov-Arnold network based on the Weisfeiler-Lehman graph kernel(KMCGKN).This method extracts the node domain subgraph and constructs the feature map with the help of the Weisfeiler-Lehman graph kernel method,and replaces the feature transformation function in the original graph convolution layer with the Kolmogorov-Arnold network.Then,two graph convolution network channels learn the characteristics of different relationship graphs respectively,thereby obtaining the feature encoding and structural encoding of the graph.At the same time,the multi-view loss ensures the diffe-rence between channels,which alleviates the overfitting problem of deep models.The KMCGKN method is evaluated on six node classification public data sets.Experimental results show that its performance in node classification tasks is better than single-channel GCN and other benchmark models,effectively improving the model modeling and representation capabilities.

Key words: Graph neural network, Weisfeiler-Lehman kernel, Kolmogorov-Arnold network, Multi-channel graph learning, Node classification

CLC Number: 

  • TP391
[1]GLIGORIJEVIC V,BAROT M,BONNEAU R.deepNF:deepnetwork fusion for protein function prediction[J].Bioinforma-tics,2018,34(22):3873-3881.
[2]FARAHANI R Z,MIANDOABCHI E,SZETO W Y,et al.A review of urban transportation network design problems[J].European journal of operational research,2013,229(2):281-302.
[3]XIAO S,WANG S,DAI Y,et al.Graph neural networks in node classification:survey and evaluation[J].Machine Vision and Applications,2022,33(1):4.
[4]WANG C,PAN S,CELINA P Y,et al.Deep neighbor-awareembedding for node clustering in attributed graphs[J].Pattern Recognition,2022,122:108230.
[5]ZHANG M,CHEN Y.Link prediction based on graph neural networks[C]//Proceedings of the 32nd International Confe-rence on Neural Information Processing Systems.2018:5171-5181.
[6]CHEN X,JIA S,XIANG Y.A review:Knowledge reasoning over knowledge graph[J].Expert Systems with Applications,2020,141:112948.
[7]YING R,HE R,CHEN K,et al.Graph convolutional neural net-works for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:974-983.
[8]SHERVASHIDZE N,SCHWEITZER P,VAN LEEUWEN EJ,et al.Weisfeiler-lehman graph kernels[J].Journal of Machine Learning Research,2011,12(9):2539-2561.
[9]VASWANI A,SHAZEER N,PARMAER N,et al.Attention is all you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.2017:6000-6010.
[10]LIU Z,WANG Y,VAIDYA S,et al.Kan:Kolmogorov-arnold networks[J].arXiv:2404.19756,2024.
[11]BRAUN J,GRIEBEL M.On a constructive proof of Kolmogo-rov’s superposition theorem[J].Constructive Approximation,2009,30:653-675.
[12]LAI M J,SHEN Z.The kolmogorov superposition theorem can break the curse of dimensionality when approximating high dimensional functions[J].arXiv:2112.09963,2021.
[13]SU Z,CHEN M,AI J,et al.Research on Recommendation Algo-rithm Based on Kolmogorov-Arnold Network-driven Scoring and Review Fusion[J].Journal of Chinese Computer Systems,2025,46(11):2600-2609.
[14]LIN R,DU S,WANG S,et al.Multi-channel augmented graph embedding convolutional network for multi-view clustering[J].IEEE Transactions on Network Science and Engineering,2023,10(4):2239-2249.
[15]ZHU X,LI C,GUO J,et al.CNIM-GCN:consensus neighbor interaction-based multi-channel graph convolutional networks[J].Expert Systems with Applications,2023,226:120178.
[16]ZHAI R,ZHANG L,WANG Y,et al.A multi-channel attention graph convolutional neural network for node classification[J].The Journal of Supercomputing,2023,79(4):3561-3579.
[17]CHAO H,CAO Y,LIU Y.Multi-channel EEG emotion recognition through residual graph attention neural network[J].Frontiers in Neuroscience,2023,17:1135850.
[18]LI J,LU G,WU Z,et al.Multi-view representation model based on graph autoencoder[J].Information Sciences,2023,632:439-453.
[19]WU Z,LIN X,LIN Z,et al.Interpretable graph convolutional network for multi-view semi-supervised learning[J].IEEE Transactions on Multimedia,2023,25:8593-8606.
[20]CHEN Y,WU Z,CHEN Z,et al.Joint learning of feature and topology for multi-view graph convolutional network[J].Neural Networks,2023,168:161-170.
[21]SHAWE-TAYLOR J,CRISTIANINI N.Kernel methods forpattern analysis[M].Cambridge:Cambridge University Press,2004.
[22]KANG U,TONG H,SUN J.Fast random walk graph kernel[C]//Proceedings of the 2012 SIAM International Conference on Data Mining.2012:828-838.
[23]BORGWARDT K M,KRIGEL H P.Shortest-path kernels on graphs[C]//Fifth IEEE International Conference on Data Mi-ning(ICDM’05).IEEE,2005.
[24]SHERVASHIDZE N,VISHWANATHAN S V N,PETRI T,et al.Efficient graphlet kernels for large graph comparison[C]//Artificial Intelligence and Statistics.PMLR,2009:488-495.
[25]CHEN D,JACOB L,MAIRAL J.Convolutional kernel networks for graph-structured data[C]//International Conference on Machine Learning.PMLR,2020:1576-1586.
[26]NAMATA G,LONDON B,GETOOR L,et al.Query-driven active surveying for collective classification[C]//10th Internatio-nal Workshop on Mining and Learning with Graphs.2012.
[27]PEI H,WEI B,CHANG K C C,et al.Geom-gcn:Geometric graph convolutional networks[J].arXiv:2002.05287,2020.
[28]TANG J,SUN J,WANG C,et al.Social influence analysis in large-scale networks[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.2009:807-816.
[29]GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.2016:855-864.
[30]PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:Online lear-ning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:701-710.
[31]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[32]VELICKOVIC P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017.
[33]HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:1025-1035.
[34]WU F,SOUZA A,ZHANG T,et al.Simplifying graph convolutional networks[C]//International Conference on Machine Learning.2019:6861-6871.
[35]ZHU J,ROSSI R A,RAO A,et al.Graph Neural Networks with Heterophily[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021:11168-11176
[36]WANG T,JIN D,WANG R,et al.Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:4210-4218.
[37]GAO H,JI S.Graph U-Nets[C]//International Conference on Machine Learning.PMLR,2019:2083-2092.
[38]WU J,CHEN X,XU K,et al.Structural entropy guided graph hierarchical pooling[C]//International Conference on Machine Learning.PMLR,2022:24017-24030.
[1] GAO Tai, REN Yanzhang, WANG Huiqing, LI Ying, WANG Bin. KGMamba:Gene Regulatory Network Prediction Model Based on Kolmogorov-Arnold Network Optimizing Graph Convolutional Network and Mamba [J]. Computer Science, 2026, 53(4): 101-111.
[2] DING Yan, DING Hongfa, YU Muran, JIANG Heling. Survey of Backdoor Attacks and Defenses on Graph Neural Network [J]. Computer Science, 2026, 53(3): 1-22.
[3] ZHAO Zhengbiao, LU Hanyu, DING Hongfa. Node-influence Based Construction Algorithm of Approximate Worst-case Forgetting Set for Graph Unlearning [J]. Computer Science, 2026, 53(3): 64-77.
[4] WANG Jinghong, LI Pengchao, WANG Xizhao, ZHANG Zili. Dual-channel Graph Neural Network Based on KAN [J]. Computer Science, 2026, 53(3): 188-196.
[5] LI Chengyu, HUANG Ke, ZHANG Ruiheng , CHEN Wei. Heterogeneous Graph Attention Network-based Approach for Smart Contract Vulnerability
Detection [J]. Computer Science, 2026, 53(2): 423-430.
[6] WANG Xinyu, SONG Xiaomin, ZHENG Huiming, PENG Dezhong, CHEN Jie. Contrastive Learning-based Masked Graph Autoencoder [J]. Computer Science, 2026, 53(2): 145-151.
[7] ZHAI Jie, CHEN Lexuan, PANG Zhiyu. Survey on Graph Neural Network-based Methods for Academic Performance Prediction [J]. Computer Science, 2026, 53(2): 16-30.
[8] YANG Ming, HE Chaobo, YANG Jiaqi. Direction-aware Siamese Network for Knowledge Concept Prerequisite Relation Prediction [J]. Computer Science, 2026, 53(2): 39-47.
[9] LIU Hongjian, ZOU Danping, LI Ping. Pedestrian Trajectory Prediction Method Based on Graph Attention Interaction [J]. Computer Science, 2026, 53(1): 97-103.
[10] LI Yaru, WANG Qianqian, CHE Chao, ZHU Deheng. Graph-based Compound-Protein Interaction Prediction with Drug Substructures and Protein 3D Information [J]. Computer Science, 2025, 52(9): 71-79.
[11] WU Hanyu, LIU Tianci, JIAO Tuocheng, CHE Chao. DHMP:Dynamic Hypergraph-enhanced Medication-aware Model for Temporal Health EventPrediction [J]. Computer Science, 2025, 52(9): 88-95.
[12] SU Shiyu, YU Jiong, LI Shu, JIU Shicheng. Cross-domain Graph Anomaly Detection Via Dual Classification and Reconstruction [J]. Computer Science, 2025, 52(8): 374-384.
[13] TANG Boyuan, LI Qi. Review on Application of Spatial-Temporal Graph Neural Network in PM2.5 ConcentrationForecasting [J]. Computer Science, 2025, 52(8): 71-85.
[14] GUO Husheng, ZHANG Xufei, SUN Yujie, WANG Wenjian. Continuously Evolution Streaming Graph Neural Network [J]. Computer Science, 2025, 52(8): 118-126.
[15] LUO Xuyang, TAN Zhiyi. Knowledge-aware Graph Refinement Network for Recommendation [J]. Computer Science, 2025, 52(7): 103-109.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.