Abstract: Graph neural networks (GNNs), a class of deep learning models designed for performing information interaction on non-Euclidean graph data, have been successfully applied to node ...

Some stories, though, were more impactful or popular with our readers than others. This article explores 15 of the biggest ...

A research team has developed a new model, PlantIF, that addresses one of the most pressing challenges in agriculture: the ...

Discover the 10 best Infrastructure as Code (IaC) tools for DevOps teams in 2025. Learn how these tools enhance automation, stability, and scalability in cloud environments. Improve your deployment ...

An interactive toolbox for standardizing, validating, simulating, reducing, and exploring detailed biophysical models that can be used to reveal how morpho-electric properties map to dendritic and ...

Want to master data fitting in Python? 📊🐍 In this video, we’ll walk you through using the least squares method to fit data and graph it using Python. Perfect for data science and stats enthusiasts!

Overview: Python supports every stage of data science from raw data to deployed systemsLibraries like NumPy and Pandas simplify data handling and analysisPython ...

GMatch4py is a library dedicated to graph matching. Graph structure are stored in NetworkX graph objects. GMatch4py algorithms were implemented with Cython to enhance performance.

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures, allows ...