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 ...

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

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, ...

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 ...