Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
M. Beyer, S. Gesper, A. Guntoro, G. Paya-Vaya, и H. Blume. Proceedings - 2023 IEEE 34th International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2023, стр. 61--68. United States, Institute of Electrical and Electronics Engineers Inc., (2023)Funding Information: This work is supported by the German federal ministry of education and research (BMBF), project ZuSE-KI-AVF (grant no. 16ME0062).; 34th IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2023 ; Conference date: 19-07-2023 Through 21-07-2023.
D. Lee, S. Yu, и H. Yu. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, стр. 1362–1370. New York, NY, USA, Association for Computing Machinery, (2020)