[17] T. Hua, W. Wang, Z. Xue, S. Ren, Y. Wang, and H. Zhao. On feature decorrelation in self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9598-9608, 2021.
[18] T. Huang, Y. Dong, M. Ding, Z. Yang, W. Feng, X. Wang, and J. Tang. Mixgcf: An improved training method for graph neural network-based recommender systems. In KDD, pages 665-674, 2021.
[19] L. Jing, P. Vincent, Y. LeCun, and Y. Tian. Understanding dimensional collapse in contrastive self-supervised learning. arXiv preprint arXiv:2110.09348, 2021.
[20] T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.
[21] P. Koniusz, F. Yan, P.-H. Gosselin, and K. Mikolajczyk. Higher-order occurrence pooling on mid-and low-level features: Visual concept detection. 2013.
[22] P. Koniusz and H. Zhang. Power normalizations in fine-grained image, few-shot image and graph classification. IEEE Trans. Pattern Anal. Mach. Intell., 44(2):591-609, 2022.
[23] B. Kulis, M. A. Sustik, and I. S. Dhillon. Low-rank kernel learning with bregman matrix divergences. Journal of Machine Learning Research, 10(2), 2009.
[24] D. Lee, S. Kang, H. Ju, C. Park, and H. Yu. Bootstrapping user and item representations for one-class collaborative filtering. In F. Diaz, C. Shah, T. Suel, P. Castells, R. Jones, and T. Sakai, editors, SIGIR, pages 1513-1522, 2021.
[25] M. Lezcano Casado. Trivializations for gradient-based optimization on manifolds. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32, 2019.
[26] L. Liang, Z. Xu, Z. Song, I. King, and J. Ye. Resnorm: Tackling long-tailed degree distribution issue in graph neural networks via normalization. CoRR, abs/2206.08181, 2022.
[27] Z. Lin, C. Tian, Y. Hou, and W. X. Zhao. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In WWW, pages 2320-2329, 2022.
[28] K. Liu, W. Tang, F. Zhou, and G. Qiu. Spectral regularization for combating mode collapse in gans. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6382-6390, 2019.
[29] Y. Ma, X. Liu, T. Zhao, Y. Liu, J. Tang, and N. Shah. A unified view on graph neural networks as graph signal denoising. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021. ACM, 2021.
[30] Y. Ma, Z. Song, X. Hu, J. Li, Y. Zhang, and I. King. Graph component contrastive learning for concept relatedness estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 13362-13370, 2023.
[31] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618, 2012.
[32] F. Ricci, L. Rokach, and B. Shapira. Introduction to recommender systems handbook. In Recommender systems handbook, pages 1-35. Springer, 2010.
[33] O. Roy and M. Vetterli. The effective rank: A measure of effective dimensionality. In 2007 15th European signal processing conference, pages 606-610. IEEE, 2007.
[34] Z. Song and I. King. Hierarchical heterogeneous graph attention network for syntax-aware summarization. In AAAI, pages 11340-11348. AAAI Press, 2022.
[35] Z. Song, Z. Meng, Y. Zhang, and I. King. Semi-supervised multi-label learning for graph-structured data. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 1723–1733, 2021.