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  1. Learning to solve graph metric dimension problem based on graph contrastive learning

    Deep learning has been widely used to solve graph and combinatorial optimization problems. However, proper model deployment is critical for training...

    Jian Wu, Li Wang, ... Fuhong Wei in Applied Intelligence
    Article 15 November 2023
  2. Improving graph-based recommendation with unraveled graph learning

    Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph...

    Chih-Chieh Chang, Diing-Ruey Tzeng, ... Chih-Ya Shen in Data Mining and Knowledge Discovery
    Article 02 June 2024
  3. Multi-behavior-based graph contrastive learning recommendation

    Graph-based collaborative filtering recommendations can more effectively explore the interaction information between users and items. However, its...

    Chenzhong Bin, Weiliang Li, ... Yimin Wen in Knowledge and Information Systems
    Article 05 March 2024
  4. Model Change Active Learning in Graph-Based Semi-supervised Learning

    Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the...

    Kevin S. Miller, Andrea L. Bertozzi in Communications on Applied Mathematics and Computation
    Article Open access 17 February 2024
  5. Debiased graph contrastive learning based on positive and unlabeled learning

    Graph contrastive learning (GCL) is one of the mainstream techniques for unsupervised graph representation learning, which reduces the distance...

    Zhiqiang Li, Jie Wang, Jiye Liang in International Journal of Machine Learning and Cybernetics
    Article 18 December 2023
  6. Iterative heterogeneous graph learning for knowledge graph-based recommendation

    Incorporating knowledge graphs into recommendation systems has attracted wide attention in various fields recently. A Knowledge graph contains...

    Liu Tieyuan, Shen Hongjie, ... Li **g**g in Scientific Reports
    Article Open access 28 April 2023
  7. Simple knowledge graph completion model based on PU learning and prompt learning

    Knowledge graphs (KGs) are important resources for many artificial intelligence tasks but usually suffer from incompleteness, which has prompted...

    Li Duan, **g Wang, ... Qiao Sun in Knowledge and Information Systems
    Article 12 January 2024
  8. A double-layer attentive graph convolution networks based on transfer learning for dynamic graph classification

    In practical scenarios, many graphs dynamically evolve over time. The new node classification without labels and historical information is...

    Article 18 August 2023
  9. Learning graph-based representations for scene flow estimation

    Scene flow estimation is a fundamental task of autonomous driving. Compared with optical flow, scene flow can provide sufficient 3D motion...

    Mingliang Zhai, Hao Gao, ... Kang Ni in Multimedia Tools and Applications
    Article 07 June 2023
  10. GNNCL: A Graph Neural Network Recommendation Model Based on Contrastive Learning

    In the field of recommendation algorithms, the representation learning for users and items has evolved from using single IDs or historical...

    **guang Chen, Jiahe Zhou, Lili Ma in Neural Processing Letters
    Article Open access 16 February 2024
  11. DC-Graph: a chunk optimization model based on document classification and graph learning

    Existing machine reading comprehension methods use a fixed stride to chunk long texts, which leads to missing contextual information at the...

    **g**g Zhou, Guohao Zhang, ... Hao Zhang in Artificial Intelligence Review
    Article Open access 16 May 2024
  12. Graph neural news recommendation based on multi-view representation learning

    Accurate news representation is of crucial importance in personalized news recommendation. Most of existing news recommendation model lack...

    **aohong Li, Ruihong Li, ... ** Yao in The Journal of Supercomputing
    Article 20 March 2024
  13. Graph-based comparative analysis of learning to rank datasets

    The relative success of learning to rank algorithms has raised the attention of the research community for develo** efficient and effective ranking...

    Article 30 June 2023
  14. A heterogeneous graph-based semi-supervised learning framework for access control decision-making

    For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security....

    Jiao Yin, Guihong Chen, ... Yuan Miao in World Wide Web
    Article Open access 24 May 2024
  15. Multi-view clustering based on graph learning and view diversity learning

    Multi-view clustering is to make full use of different views of the data for clustering. In recent years, many multi-view clustering methods have...

    Lin Wang, Dong Sun, ... Yixiang Lu in The Visual Computer
    Article 14 November 2022
  16. BotCL: a social bot detection model based on graph contrastive learning

    The proliferation of social bots on social networks presents significant challenges to network security due to their malicious activities. While...

    Yan Li, Zhenyu Li, ... Haoyu Lu in Knowledge and Information Systems
    Article 26 April 2024
  17. Graph learning-based generation of abstractions for reinforcement learning

    The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial explosion of the state space. Previous works have...

    Yuan Xue, Daniel Kudenko, Megha Khosla in Neural Computing and Applications
    Article Open access 09 February 2023
  18. Graph Contrastive Learning with Constrained Graph Data Augmentation

    Studies on graph contrastive learning, which is an effective way of self-supervision, have achieved excellent experimental performance. Most existing...

    Shaowu Xu, Luo Wang, **bin Jia in Neural Processing Letters
    Article 17 August 2023
  19. Task-related network based on meta-learning for few-shot knowledge graph completion

    Knowledge graph (KG) is a powerful tool in many areas, but it is impossible to take in all knowledge during construction for the complexity of...

    Xu-Hua Yang, Dong Wei, ... Hai-**a Long in Applied Intelligence
    Article 01 April 2024
  20. Large-scale knowledge graph representation learning

    The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks....

    Marwa Badrouni, Chaker Katar, Wissem Inoubli in Knowledge and Information Systems
    Article 29 May 2024
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