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).
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.
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…
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.
O. Corby, R. Dieng, and C. Hébert. Conceptual Structures: Logical, Linguistic, and Computational
Issues, 8th International Conference on Conceptual Structures,
ICCS 2000, Darmstadt, Germany, August 14-18, 2000, Proceedings, volume 1867 of LNCS, page 468--482. Springer, (2000)