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
IEEE/CAA Journal of Automatica Sinica
| 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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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