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 ...

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 ...

"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 ...

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 ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

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, ...

The concept of knowledge graphs arose from scientific advances in a variety of research fields, including the semantic web, databases, natural language processing, and machine learning. According to ...

Social media can be a valuable learning resource for business students because it can help them connect complex theories with day-to-day business decisions and facilitate a greater understanding of ...