The decline in religiosity over the past 15 years is twice as great as the decline in 1960s and 1970s. An update through 2013 is now available here. Religiosity in the United States is in the midst of ...

Abstract: Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and ...

Abstract: Graph theory and machine learning are revolutionary approaches to medical image analysis that leverage the structural nuances of medical data for better diagnostic accuracy. This research ...

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometrics 2.3.1. Note that ${PROJECT_DIR} refers to this directory. The following section outlines the graph-to-graph transformation ...

Welcome to the complete code implementation for the book Hands-On Graph Neural Networks Using Python. This repository contains all the code examples from the book, organized into chapters for easy ...