B, Perozzi R, Al-Rfou S S Skiena . DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014
2
S, Yan D, Xu B, Zhang H, Zhang Q, Yang S Lin . Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29( 1): 40–51
3
R, Bommasani D A, Hudson E, Adeli R, Altman S, Arora , et al.. On the opportunities and risks of foundation models. 2021, arXiv preprint arXiv: 2108.07258
4
J, Wei Y, Tay R, Bommasani C, Raffel B, Zoph S, Borgeaud D, Yogatama M, Bosma D, Zhou D, Metzler E H, Chi T, Hashimoto O, Vinyals P, Liang J, Dean W Fedus . Emergent abilities of large language models. Transactions on Machine Learning Research, 2022,
5
J, Liu C, Yang Z, Lu J, Chen Y, Li M, Zhang T, Bai Y, Fang L, Sun P S, Yu C Shi . Towards graph foundation models: a survey and beyond. 2023, arXiv preprint arXiv: 2310.11829
6
Y, Zhu Y, Xu F, Yu Q, Liu S, Wu L Wang . Deep graph contrastive representation learning. 2020, arXiv preprint arXiv: 2006.04131
7
Z, Hou X, Liu Y, Cen Y, Dong H, Yang C, Wang J Tang . GraphMAE: self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022
8
A, Gui J, Ye H Xiao . G-adapter: towards structure-aware parameter-efficient transfer learning for graph transformer networks. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2023
9
M, Sun K, Zhou X, He Y, Wang X Wang . GPPT: graph pre-training and prompt tuning to generalize graph neural networks. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022
10
X, Wang D, Wang L, Chen F, Wang Y Lin . Building transportation foundation model via generative graph transformer. In: Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC). 2023
