Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

Selecting the right material from countless possibilities remains a central hurdle in materials discovery. Theory-driven ...

The road to competitive advantage and differentiation based on learning to think in graphs is going to be different for each company. But it is a road worth traveling.

This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning ...

Alongside text-based large language models (LLMs), including ChatGPT in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial ...

Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

The idea is that graph networks are bigger than any one machine-learning approach. Graphs bring an ability to generalize about structure that the individual neural nets don't have.