A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 11 Issue 7
Jul.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
J. Chen, Y. Yuan, and X. Luo, “SDGNN: Symmetry-preserving dual-stream graph neural networks,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1717–1719, Jul. 2024. doi: 10.1109/JAS.2024.124410
Citation: J. Chen, Y. Yuan, and X. Luo, “SDGNN: Symmetry-preserving dual-stream graph neural networks,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 7, pp. 1717–1719, Jul. 2024. doi: 10.1109/JAS.2024.124410

SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

doi: 10.1109/JAS.2024.124410
More Information
  • loading
  • [1]
    M. Welling and T. N. Kipf, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv: 1609.02907, 2016.
    [2]
    W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proc. Adv. Neural Inf. Proc. Syst., 2017, pp. 1024−1034.
    [3]
    P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proc. Int. Conf. Learn. Representations, 2018, pp. 1−12.
    [4]
    C. Huang, M. Li, F. Cao, et al., “Are graph convolutional networks with random weights feasible?” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 3, pp. 2751−2768, 2023.
    [5]
    C. Zou, A. Han, L. Lin, M. Li, and J. Gao, “A simple yet effective framelet-based graph neural network for directed graphs,” IEEE Trans. Artif. Intell., vol. 5, no. 4, pp. 1647−1657, 2024.
    [6]
    T. Kong, T. Kim, J. S. Jeon, J. W. Choi, Y. C. Lee, N. Park, and S. W. Kim, “Linear, or non-linear, that is the question!” in Proc. 15th ACM Int. Conf. Web Search and Data Min., 2022, pp. 517−525.
    [7]
    L. Chen, L. Wu, R. Hong, et al., “Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 27−34.
    [8]
    X. He, K. Deng, X. Wang, et al., “LightGCN: Simplifying and powering graph convolution network for recommendation,” in Proc. 43rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2020, pp. 639−648.
    [9]
    X. Liu, M. Yan, L. Deng, et al., “Sampling methods for efficient training of graph convolutional networks: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 205−234, 2022.
    [10]
    T. He, S. O. Yew, and B. Lu, “Learning conjoint attentions for graph neural nets,” in Proc. Adv. Neural Inf. Process. Syst., 2021, pp. 2641−2653.
    [11]
    Z. G. Liu, X. Luo, and M. Zhou, “Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 2, pp. 1406−1420, 2024.
    [12]
    F. Bi, T. He, Y. Xie, and X. Luo, “Two-stream graph convolutional network-incorporated latent feature analysis,” IEEE Trans. Serv. Comput., vol. 16, no. 4, pp. 3027−3042, 2023.
    [13]
    X. Luo, D. Wang, M. Zhou, and H. Yuan, “Latent factor-based recommenders relying on extended stochastic gradient descent algorithms,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 2, pp. 916−926, 2021.
    [14]
    T. A. Davis and Y. F. Hu, “The university of Florida sparse matrix collection,” ACM Trans. Math. Softw., vol. 38, no. 1, pp. 1−25, 2011.
    [15]
    X. He, L. Liao, H. Zhang, et al., “Neural collaborative filtering,” in Proc. Int. Conf. World Wide Web, 2017, pp. 173−182.
    [16]
    R. V. Berg, T. N. Kipf, and M. Welling, “Graph convolutional matrix completion,” arXiv preprint arXiv: 1706.02263, 2017.
    [17]
    W. Guo, Y. Yang, Y. Hu, et al., “Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation”, in Proc. Workshop Deep Learning Practice for High-Dimensional Sparse Data With KDD, 2021.
    [18]
    J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, and X. Xie, “Self-supervised graph learning for recommendation,” in Proc. 44th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2021, pp. 726−735.
    [19]
    M. Zhao, L. Wu, Y. Liang, et al., “Investigating accuracy-novelty performance for graph-based collaborative filtering,” in Proc. 45th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2022, pp. 50−59.
    [20]
    S. Peng, K. Sugiyama, and T. Mine, “Less is more: Reweighting important spectral graph features for recommendation,” in Proc. 45th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 2022, pp. 1273−1282.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(3)

    Article Metrics

    Article views (129) PDF downloads(27) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return