Fullstack GraphQL Tutorial to go from zero to production covering all basics and advanced concepts. Includes tutorials for Apollo, Relay, React and NodeJS.
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.
TL;DR: Have you even wondered what is so special about convolution? In this post, I derive the convolution from first principles and show that it naturally emerges from translational symmetry. During…
Graph neural networks are intimately related to partial differential equations governing information diffusion on graphs. Thinking of GNNs as PDEs leads to a new broad class of graph ML methods.
Relational data represent relationships between entities anywhere on the web (e.g. online social networks) or in the physical world (e.g. structure of the protein).
H. Nguyen, N. Nguyen, H. Doan, Z. Ahmadi, T. Doan, и L. Jiang. Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, New York, NY, USA, Association for Computing Machinery, (ноября 2022)
X. Liu, T. Zhu, H. Tan, и R. Zhang. The Semantic Web--ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23--27, 2022, Proceedings, стр. 284--302. Springer, (2022)
R. Dindokar, N. Choudhury, и Y. Simmhan. IEEE International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning), Co-located with IPDPS, стр. 1185--1190. (2015)Short paper.
P. Heim, J. Ziegler, и S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), том 417 из CEUR Workshop Proceedings, стр. 49--58. Aachen, (2008)
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, и K. Weinberger. Proceedings of the 36th International Conference on Machine Learning, том 97 из Proceedings of Machine Learning Research, стр. 6861--6871. PMLR, (09--15 Jun 2019)
J. Feng, Y. Chen, F. Li, A. Sarkar, и M. Zhang. Advances in Neural Information Processing Systems, 35, стр. 4776--4790. Curran Associates, Inc., (2022)
X. Wang, и M. Zhang. Proceedings of the 39th International Conference on Machine Learning, том 162 из Proceedings of Machine Learning Research, стр. 23341--23362. PMLR, (17--23 Jul 2022)
D. Yang, P. Rosso, B. Li, и P. Cudre-Mauroux. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, стр. 1162–1172. New York, NY, USA, Association for Computing Machinery, (2019)
J. Zhang, Y. Dong, Y. Wang, J. Tang, и M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, стр. 4278–4284. AAAI Press, (10.08.2019)
P. Heim, J. Ziegler, и S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), том 417 из CEUR Workshop Proceedings, стр. 49--58. Aachen, (2008)
Y. Yang, C. Huang, L. Xia, и C. Li. Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, стр. 1434--1443. (2022)