[43] Ilya Loshchilov and Frank Hutter. Online batch selection for faster training of neural networks. arXiv preprint arXiv:1511.06343, 2015.
[44] Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi. Learning to pre-train graph neural networks. In AAAI, pages 4276-4284, 2021.
[45] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In NeurIPS, pages 3111-3119, 2013.
[46] Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005, 2017.
[47] Mark E. J. Newman. The structure and function of complex networks. SIAM Reviews, 45:167-256, 2003.
[48] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
[49] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In SIGKDD, pages 701-710, 2014.
[50] Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. GCC: Graph contrastive coding for graph neural network pre-training. In SIGKDD, pages 1150-1160, 2020.
[51] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020.
[52] Tiago Ramalho and Miguel Miranda. Density estimation in representation space to predict model uncertainty. In Engineering Dependable and Secure Machine Learning Systems: Third International Workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020, Revised Selected Papers 3, pages 84-96. Springer, 2020.
[53] Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo. struc2vec: Learning node representations from structural identity. In SIGKDD, pages 385-394, 2017.
[54] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In CVPR, pages 815-823, 2015.
[55] Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick. Training region-based object detectors with online hard example mining. In CVPR, pages 761-769, 2016.
[56] Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, and Francesc Moreno-Noguer. Fracking deep convolutional image descriptors. arXiv preprint arXiv:1412.6537, 2014.
[57] Stavros Sintos and Panayiotis Tsaparas. Using strong triadic closure to characterize ties in social networks. In SIGKDD, pages 1466-1475, 2014.
[58] Yumin Suh, Bohyung Han, Wonsik Kim, and Kyoung Mu Lee. Stochastic class-based hard example mining for deep metric learning. In CVPR, pages 7244-7252, 2019.
[59] Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR, 2020.
[60] Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou. MoCL: Data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph. In SIGKDD, pages 3585-3594, 2021.
[61] Natasa Tagasovska and David Lopez-Paz. Single-model uncertainties for deep learning. NeurIPS, 32, 2019.
[62] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105-6114, 2019.
[63] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiao zhu Mei. Line: Large-scale information network embedding. In WWW, page 1067-1077, 2015.