Sen Wu, Hongyang Zhang, and Christopher Ré. Understanding and improving information transfer in multi-task learning. *ICLR*, 2020.
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay S. Pande. Moleculenet: A benchmark for molecular machine learning. arXiv: Learning, 2017.
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z. Li. Simgrace: A simple framework for graph contrastive learning without data augmentation. Proceedings of the ACM Web Conference 2022, 2022a.
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z Li. A survey of pretraining on graphs: Taxonomy, methods, and applications. arXiv preprint arXiv:2202.07893, 2022b.
Yinghui Xing, Qirui Wu, De Cheng, Shizhou Zhang, Guoqiang Liang, and Yanning Zhang. Class-aware visual prompt tuning for vision-language pre-trained model. ArXiv, abs/2208.08340, 2022.
Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, and Zhibin Xiao. Rethinking network pruning - under the pre-train and fine-tune paradigm. *NAACL*, 2021a.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? ICLR, 2019.
Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang. Self-supervised graph-level representation learning with local and global structure. In International Conference on Machine Learning, 2021b.
Pinar Yanardag and S. V. N. Vishwanathan. Deep graph kernels. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015.
Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, and Haggai Maron. From local structures to size generalization in graph neural networks. In ICML, 2021.
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. NeurIPS, 2020.
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. Graph contrastive learning automated. In International Conference on Machine Learning, pages 12121-12132. PMLR, 2021.
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. Bringing your own view: Graph contrastive learning without prefabricated data augmentations. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022.
Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, N. Chawla, and Huan Liu. Few-shot learning on graphs. In International Joint Conference on Artificial Intelligence, 2022.
Muhan Zhang and Pan Li. Nested graph neural networks. NeurIPS, 2021.
Richard Zhang, Phillip Isola, and Alexei A. Efros. Colorful image colorization. In ECCV, 2016.
Z. Zhang, Q. Liu, H. Wang, C. Lu, and C. K. Lee. Motif-based graph self-supervised learning for molecular property prediction. 2021a.
Zaixin Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. Motif-based graph self-supervised learning for molecular property prediction. In Neural Information Processing Systems, 2021b.
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah. From stars to subgraphs: Uplifting any gnn with local structure awareness. ICLR, 2022.
Fan Zhou and Chengtai Cao. Overcoming catastrophic forgetting in graph neural networks with experience replay. In AAAI Conference on Artificial Intelligence, 2021.
Marinka Zitnik, Rok Sosi, and Jure Leskovec. Prioritizing network communities. Nature Communications, 9, 2018.