Graph technology is approaching an inflection point in its journey from an interesting new type of database to an essential tool for enterprise workloads. The progression graph technology is taking ...

The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we ...

Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...

Graph database developer Neo4j Inc. is upping its machine learning game today with a new release of Neo4j for Graph Data Science framework that leverages deep learning and graph convolutional neural ...

Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in ...

"This is what we need to do. It's not popular right now, but this is why the stuff that is popular isn't working." That's a gross oversimplification of what scientist, best-selling author, and ...