[45] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118-22133, 2020.
[46] Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. A comprehensive survey of neural architecture search: Challenges and solutions. ACM Computing Surveys (CSUR), 54(4):1-34, 2021.
[47] Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, and Arun K Somani. Neural architecture search for transformers: A survey. IEEE Access, 10:108374-108412, 2022.
[48] Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, and Wenwu Zhu. Autogl: A library for automated graph learning. In ICLR 2021 Workshop CTRL, 2021.
[49] Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, and Wenwu Zhu. Nas-bench-graph: Benchmarking graph neural architecture search. arXiv preprint arXiv:2206.09166, 2022.
[50] Ziwei Zhang, Peng Cui, and Wenwu Zhu. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 34(1):249-270, 2020.
[51] Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu. Large graph models: A perspective. arXiv preprint arXiv:2308.14522, 2023.
[52] Zeyang Zhang, Ziwei Zhang, Xin Wang, and Wenwu Zhu. Learning to solve travelling salesman problem with hardness-adaptive curriculum. In Thirty-Fifth AAAI Conference on Artificial Intelligence, 2022.
[53] Ziwei Zhang, Xin Wang, Zeyang Zhang, Peng Cui, and Wenwu Zhu. Revisiting transformation invariant geometric deep learning: Are initial representations all you need? arXiv preprint arXiv:2112.12345, 2021.
[54] Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Simin Wu, and Wenwu Zhu. Llm4dyg: Can large language models solve problems on dynamic graphs? arXiv preprint, 2023.
[55] Kaixiong Zhou, Qingquan Song, Xiao Huang, and Xia Hu. Auto-gnn: Neural architecture search of graph neural networks. arXiv:1909.03184, 2019.
[56] Matheus Nunes and Gisele L Pappa. Neural architecture search in graph neural networks. In Brazilian Conference on Intelligent Systems, pages 302-317, 2020.
[57] Min Shi, David A Wilson, Xingquan Zhu, Yu Huang, Yuan Zhuang, Jianxun Liu, and Yufei Tang. Genetic-gnn: Evolutionary architecture search for graph neural networks. Knowledge-Based Systems, page 108752, 2022.
[58] Wentao Zhang, Zheyu Lin, Yu Shen, Yang Li, Zhi Yang, and Bin Cui. Deep and flexible graph neural architecture search. In International Conference on Machine Learning, pages 26362-26374. PMLR, 2022.
[59] Yuhui Ding, Quanming Yao, Huan Zhao, and Tong Zhang. Diffmg: Differentiable meta graph search for heterogeneous graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 279-288, 2021.
[60] ZHAO Huan, YAO Quanming, and TU Weiwei. Search to aggregate neighborhood for graph neural network. In 2021 IEEE 37th International Conference on Data Engineering, pages 552-563, 2021.
[61] Yanxi Li, Zean Wen, Yunhe Wang, and Chang Xu. One-shot graph neural architecture search with dynamic search space. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 8510-8517, 2021.
[62] Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, and Qingming Huang. Rethinking graph neural architecture search from message-passing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6657-6666, 2021.
[63] Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, and Wenwu Zhu. Graph neural architecture search under distribution shifts. In International Conference on Machine Learning, pages 18083-18095. PMLR, 2022.
[64] Zeyang Zhang, Ziwei Zhang, Xin Wang, Yijian Qin, Zhou Qin, and Wenwu Zhu. Dynamic heterogeneous graph attention neural architecture search. In Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023.