Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, and Jieren Cheng. Deep fusion clustering network. In 35th AAAI Conference on Artificial Intelligence, pages 9978-9987, 2021.
Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(11), 2008.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations, 2018.
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R. Devon Hjelm. Deep graph infomax. In Proceedings of the 7th International Conference on Learning Representations, 2019.
Ulrike Von Luxburg. A tutorial on spectral clustering. Statistics and computing, 17:395-416, 2007.
Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, and Jian Pei. AM-GCN: adaptive multi-channel graph convolutional networks. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1243-1253, 2020.
Yifei Wang, Yisen Wang, Jiansheng Yang, and Zhouchen Lin. Dissecting the diffusion process in linear graph convolutional networks. In Advances in Neural Information Processing Systems, pages 5758-5769, 2021.
Shiping Wang, Zhihao Wu, Yuhong Chen, and Yong Chen. Beyond graph convolutional network: An interpretable regularizer-centered optimization framework. In Proceedings of the 37th AAAI Conference on Artificial Intelligence., pages 4693-4701, 2023.
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. Simplifying graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning, pages 6861-6871, 2019.
Zhebin Wu, Lin Shu, Ziyue Xu, Yaomin Chang, Chuan Chen, and Zibin Zheng. Robust tensor graph convolutional networks via T-SVD based graph augmentation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2090-2099, 2022.
Zhihao Wu, Xincan Lin, Zhenghong Lin, Zhaoliang Chen, Yang Bai, and Shiping Wang. Interpretable graph convolutional network for multi-view semi-supervised learning. IEEE Transactions on Multimedia, pages 1-14, 2023.
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. Representation learning on graphs with jumping knowledge networks. In Proceedings of the 35th International Conference on Machine Learning, pages 5449-5458, 2018.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In Proceedings of the 7th International Conference on Learning Representations, 2019.
Bingbing Xu, Bin Xie, and Huawei Shen. Towards deeper graph neural networks via layer-adaptive. In Proceedings of the Web Conference (WWW), pages 103-106, 2023.
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In Proceedings of the 33rd International Conference on Machine Learning, pages 40-48, 2016.
Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, and Baocai Yin. A new perspective on the effects of spectrum in graph neural networks. In Proceedings of the 39th International Conference on Machine Learning, pages 25261-25279, 2022.
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. An end-to-end deep learning architecture for graph classification. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pages 4438-4445, 2018.