Graph foundation model

Graphical abstract

References

[1] Perozzi B, Al-Rfou R, Skiena S S. DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014

[2] Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S . Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29( 1): 40–51

[3] Bommasani R, Hudson D A, Adeli E, Altman R, Arora S, , . On the opportunities and risks of foundation models. 2021, arXiv preprint arXiv: 2108.07258

[4] Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D, Chi E H, Hashimoto T, Vinyals O, Liang P, Dean J, Fedus W . Emergent abilities of large language models. Transactions on Machine Learning Research, 2022,

[5] Liu J, Yang C, Lu Z, Chen J, Li Y, Zhang M, Bai T, Fang Y, Sun L, Yu P S, Shi C. Towards graph foundation models: a survey and beyond. 2023, arXiv preprint arXiv: 2310.11829

[6] Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L. Deep graph contrastive representation learning. 2020, arXiv preprint arXiv: 2006.04131

[7] Hou Z, Liu X, Cen Y, Dong Y, Yang H, Wang C, Tang J. GraphMAE: self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022

[8] Gui A, Ye J, Xiao H. 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] Sun M, Zhou K, He X, Wang Y, Wang X. 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] Wang X, Wang D, Chen L, Wang F, Lin Y. Building transportation foundation model via generative graph transformer. In: Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC). 2023

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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