Process Network Synthesis (PNS) and the associated P-graph methodology represent a rigorous, graph‐theoretic framework for the systematic design, analysis and optimisation of process systems.

Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

You wouldn’t change up your entire production process based on sales from just a couple of locations, and you wouldn’t lower auto insurance premiums across the board because collision rates went down ...

DPABINet, a sophisticated enhancement of the DPABI software suite, streamlines the intricate analysis of brain networks through fMRI data, providing researchers of all expertise levels with ...

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, ...

Showing numerical data graphically is crucial whenever there are more than half-a-dozen data points – the human mind (at least of most of us) simply can’t grasp an array of values and see the ...

How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in ...

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 ...

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...