[20] Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, and Xia Hu. Policy-gnn: Aggregation optimization for graph neural networks. In KDD, pages 461-471. ACM, 2020.
[21] Junying Li, Deng Cai, and Xiaofei He. Learning graph-level representation for drug discovery. CoRR, abs/1709.03741, 2017.
[22] Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res., 18:185:1-185:52, 2017.
[23] Petro Liashchynskyi and Pavlo Liashchynskyi. Grid search, random search, genetic algorithm: A big comparison for NAS. CoRR, abs/1912.06059, 2019.
[24] David Liben-Nowell and Jon M. Kleinberg. The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol., 58(7):1019-1031, 2007.
[25] Bin Luo, Richard C. Wilson, and Edwin R. Hancock. Spectral embedding of graphs. Pattern Recognit., 36(10):2213-2230, 2003.
[26] Mustafa Misir and Michèle Sebag. Alors: An algorithm recommender system. Artif. Intell., 244:291-314, 2017.
[27] Mladen Nikolic, Filip Marić, and Predrag Janić. Simple algorithm portfolio for sat. Artificial Intelligence Review, 40(4):457-465, 2013.
[28] Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos. MultiImport: Inferring node importance in a knowledge graph from multiple input signals. In KDD, pages 503-512. ACM, 2020.
[29] Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, and Yuxiao Dong. EvoKG: Jointly modeling event time and network structure for reasoning over temporal knowledge graphs. In WSDM, pages 794-803. ACM, 2022.
[30] Namyong Park, Ryan A. Rossi, Nesreen Ahmed, and Christos Faloutsos. MetaGL: Evaluation-free selection of graph learning models via meta-learning. In The Eleventh International Conference on Learning Representations, 2023.
[31] Namyong Park, Ryan A. Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen K. Ahmed, and Christos Faloutsos. CGC: Contrastive graph clustering for community detection and tracking. In WWW, pages 1115-1126. ACM, 2022.
[32] Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, and Wenwu Zhu. Nas-bench-graph: Benchmarking graph neural architecture search. In NeurIPS, 2022.
[33] Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. Practical bayesian optimization of machine learning algorithms. In NIPS, pages 2960-2968, 2012.
[34] Chang Su, Jie Tong, Yongjun Zhu, Peng Cui, and Fei Wang. Network embedding in biomedical data science. Briefings Bioinform., 21(1):182-197, 2020.
[35] Shyam A. Tailor, Felix L. Opolka, Pietro Lio, and Nicholas Donald Lane. Do we need anisotropic graph neural networks? In ICLR. OpenReview.net, 2022.
[36] Ke Tu, Jianxin Ma, Peng Cui, Jian Pei, and Wenwu Zhu. AutoNE: Hyperparameter optimization for massive network embedding. In KDD, pages 216-225. ACM, 2019.
[37] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. In ICLR (Poster). OpenReview.net, 2018.
[38] Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, and R. Devon Hjelm. Deep graph infomax. In ICLR (Poster). OpenReview.net, 2019.
[39] Yining Wang, Liwei Wang, Yuanzhi Li, Di He, and Tie-Yan Liu. A theoretical analysis of NDCG type ranking measures. In  $COLT$ , volume 30 of JMLR Workshop and Conference Proceedings, pages 25-54. JMLR.org, 2013.