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 ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...

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 ...

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 ...

A graph structure is extremely useful for predicting properties of its constituents. The most successful way of performing this prediction is to map each entity to a vector through the use of deep ...

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 ...

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of agricultural watersheds. To address this need, a team of researchers led by the ...