Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Graph neural networks operate on graph structures, as opposed to other types of neural networks that operate on vectors. What this means in practice is that they can leverage additional information.

Expect to hear increasing buzz around graph neural network use cases among hyperscalers in the coming year. Behind the scenes, these are already replacing existing recommendation systems and traveling ...

Stephen P. Curram, John Mingers, Neural Networks, Decision Tree Induction and Discriminant Analysis: An Empirical Comparison, The Journal of the Operational Research ...

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, ...

To address these limitations, we introduce a novel framework: the Molecular Merged Hypergraph Neural Network (MMHNN). MMHNN ...