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.
Fullstack GraphQL Tutorial to go from zero to production covering all basics and advanced concepts. Includes tutorials for Apollo, Relay, React and NodeJS.
P. Heim, J. Ziegler, und S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), Volume 417 von CEUR Workshop Proceedings, Seite 49--58. Aachen, (2008)
D. Peng, A. Wolff, und J. Haunert. Proc. 28th Int. Cartogr. Conf. (ICC'17) -- Advances
in Cartogr. & GIScience, Seite 389--404. Springer-Verlag, (2017)
Y. Yang, C. Huang, L. Xia, und C. Li. Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, Seite 1434--1443. (2022)
J. Zhang, Y. Dong, Y. Wang, J. Tang, und M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Seite 4278–4284. AAAI Press, (10.08.2019)
D. Yang, P. Rosso, B. Li, und P. Cudre-Mauroux. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Seite 1162–1172. New York, NY, USA, Association for Computing Machinery, (2019)