Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years ...

Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural ...

In this article I'll show you how to do time series regression using a neural network, with "rolling window" data, coded from scratch, using Python. A good way to see where this article is headed is ...

Over the past few years the artificial intelligence community has shown an increasing interest in deep learning research on graph-structured data. Many neural network models on graphs — or graph ...

A graph neural network (GNN) is a neural network that operates on graphs and learns from their structure and features. GNNs can be seen as a generalization of convolutional neural networks (CNNs ...

Understanding Neural Network Model Overfitting Model overfitting is a significant problem when training neural networks. The idea is illustrated in the graph in Figure 2. There are two predictor ...