基于对比学习和结构更新机制的异质图结构学习

doi:10.16451/j.cnki.issn1003-6059.202505005

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1332 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Heterogeneous graph neural networks hold significant advantages in complex graph data mining tasks. However, existing methods typically follow a supervised learning paradigm. Therefore, they are highly dependent on node labeling information and sensitive to noisy links in the original graph structure. As a result, their applications in labeling-scarce scenarios are limited. To address these issues, a method for heterogeneous graph structure learning based on contrastive learning and structure update mechanism(HGSL-CL) is proposed. The learning target is first generated as the anchor view from the original data. The type-aware feature mapping and weighted multi-view similarity computation are combined to generate the learner view. Subsequently, the anchor view is iteratively optimized through the structure update mechanism, and the node representations in two views are obtained using semantic-level attention. Finally, node representations from both views are projected into a shared latent space via a multi-layer perceptron. The graph structure optimization is achieved by the cross-view synergistic contrastive loss function, and a positive sample filtering strategy fusing node topological similarity and attribute similarity is introduced to enhance the discriminative ability of contrastive learning. Experiments on three datasets show that HGSL-CL outperforms other baseline models in node classification and clustering tasks. Moreover, the learned graph structure can be generalized to semi-supervised scenarios, and HGSL-CL achieves better performance than the original baseline models. The results demonstrate the effectiveness of graph structure learning. The source code of HGSL-CL is available at https://github.com/desslie047/HGSL-CL.

Key words： Heterogeneous Graph Neural Network Contrastive Learning Graph Structure Learning Self-Supervised Learning Meta-Path

Received: 25 February 2025

ZTFLH:

TP39

Fund:National Key Research and Development Program of China(No.2022ZD0119501), Natural Science Foundation of Shandong Province(No.ZR2022MF268), Humanities and Social Sciences Foundation of Ministry of Education of China(No.24YJAZH058)

Corresponding Authors: LI Chao, Ph.D.,professor.His research interests include network representation learning,social network analysis and recommendation algorithms.

About author:: GUO Ningyuan, Master student.His research interests include graph representation learning and heterogeneous graph neural networks.
SUN Guoyi, Master student.His research interests include machine learning and heterogeneous graph neural networks.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	GUO Ningyuan
	SUN Guoyi
	LI Chao

Cite this article:

GUO Ningyuan,SUN Guoyi,LI Chao. Heterogeneous Graph Structure Learning Based on Contrastive Learning and Structure Update Mechanism[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(5): 442-456.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202505005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I5/442

[1] 章淯淞,夏鸿斌,刘渊.结合自对比图神经网络与双预测器的会话推荐模型.模式识别与人工智能, 2024, 37(3): 242-252.
(ZHANG Y S, XIA H B, LIU Y.Session-Based Recommendation Model with Self Contrastive Graph Neural Network and Dual Predictor. Pattern Recognition and Artificial Intelligence, 2024, 37(3):242-252.)
[2] 张玉朋,李香菊,李超,等.基于Transformer与异质图神经网络的新闻推荐模型.模式识别与人工智能, 2022, 35(9): 839-848.
(ZHANG Y P, LI X J, LI C, et al. News Recommendation Model Based on Transformer and Heterogeneous Graph Neural Network. Pattern Recognition and Artificial Intelligence, 2022, 35(9): 839-848.)
[3] 邬锦琛,杨兴耀,于炯,等.双通道异构图神经网络序列推荐算法.计算机科学与探索, 2023, 17(6): 1473-1486.
(WU J C, YANG X Y, YU J, et al. Sequence Recommendation with Dual Channel Heterogeneous Graph Neural Network. Journal of Frontiers of Computer Science and Technology, 2023, 17(6): 1473-1486.)
[4] ZHANG C X, SONG D J, HUANG C, et al. Heterogeneous Graph Neural Network // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2019: 793-803.
[5] WANG X, JI H Y, SHI C, et al. Heterogeneous Graph Attention Network // Proc of the World Wide Web Conference. New York, USA: ACM, 2019: 2022-2032.
[6] HU Z N, DONG Y X, WANG K S, et al. Heterogeneous Graph Transformer // Proc of the Web Conference. New York, USA: ACM, 2020: 2704-2710.
[7] FU X Y, ZHANG J N, MENG Z Q, et al. MAGNN: Metapath Aggre-gated Graph Neural Network for Heterogeneous Graph Embedding // Proc of the Web Conference. New York, USA: ACM, 2020: 2331-2341.
[8] LIU Y X, ZHENG Y, ZHANG D K, et al. Towards Unsupervised Deep Graph Structure Learning // Proc of the ACM Web Confe-rence. New York, USA: ACM, 2022: 1392-1403.
[9] 李雅琪,王杰,王锋,等.基于层次对比学习的半监督节点分类算法.模式识别与人工智能, 2023, 36(8): 712-720.
(LI Y Q, WANG J, WANG F, et al. Semi-supervised Node Classification Algorithm Based on Hierarchical Contrastive Learning. Pa-ttern Recognition and Artificial Intelligence, 2023, 36(8): 712-720.)
[10] HU Y F, ZHANG Y.Graph Contrastive Learning with Local and Global Mutual Information Maximization // Proc of the 8th International Conference on Information Technology(IoT and Smart City). New York, USA: ACM, 2021: 74-78.
[11] JIN W, MA Y, LIU X R, et al. Graph Structure Learning for Robust Graph Neural Networks // Proc of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2020: 66-74.
[12] FRANCESCHI L, NIEPERT M, PONTIL M, et al. Learning Discrete Structures for Graph Neural Networks. Proceedings of Machine Learning Research, 2019, 97: 1972-1982.
[13] JIANG B, ZHANG Z Y, LIN D D, et al. Semi-supervised Lear-ning with Graph Learning-Convolutional Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 11305-11312.
[14] CHEN Y, WU L F, ZAKI M J.Deep Iterative and Adaptive Lear-ning for Graph Neural Networks[C/OL]. [2025-02-24].https://arxiv.org/pdf/1912.07832.
[15] SHI C, YU P S.Heterogeneous Information Network Analysis and Applications. Berlin, Germany: Springer, 2017.
[16] DONG Y X, CHAWLA N V, SWAMI A. metapath2vec: Scalable Representation Learning for Heterogeneous Networks // Proc of the 23rd ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining. New York, USA: ACM, 2017: 135-144.
[17] REN Y X, LIU B, HUANG C, et al. Heterogeneous Deep Graph Infomax[C/OL].[2025-02-24]. https://arxiv.org/pdf/1911.08538.
[18] PARK C, KIM D, HAN J W, et al. Unsupervised Attributed Multiplex Network Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 5371-5378.
[19] WANG X, LIU N, HAN H, et al. Self-Supervised Heterogeneous Graph Neural Network with Co-contrastive Learning // Proc of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2021: 1726-1736.
[20] ZHAO J N, WANG X, SHI C, et al. Network Schema Preserving Heterogeneous Information Network Embedding // Proc of the 29th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2020: 1366-1372.
[21] WANG Z H, LI Q, YU D H, et al. Heterogeneous Graph Contrastive Multi-view Learning // Proc of the SIAM International Confe-rence on Data Mining. Philadelphia, USA: SIAM, 2023: 136-144.
[22] YU J X, GE Q Q, LI X, et al. Heterogeneous Graph Contrastive Learning with Meta-Path Contexts and Adaptively Weighted Negative Samples. IEEE Transactions on Knowledge and Data Enginee-ring, 2024, 36(10): 5181-5193.
[23] YANG Y M, GUAN Z Y, LI J X, et al. Interpretable and Efficient Heterogeneous Graph Convolutional Network. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(2): 1637-1650.