In our book Graph Data Modelling in Python, you will learn how to design, implement and utilize a variety of graph data models, using the open-source Python libraries NetworkX and igraph. Following ...

When you customize graphs in Python, you transform raw data into compelling narratives. Python, with its rich libraries like Matplotlib and Seaborn, provides extensive options for graph customization.

On smaller scales, businesses all have networks of data. By leveraging only standard business intelligence approaches, you only see the metrics; network or knowledge graphs open up the possibility of ...

Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph.

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 ...

Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. ... Explore common network science tools in Python; Discover how geometry impacts ...

Learn about some of the common network analysis tasks and methods that you can use or recommend in Python, such as data collection, metrics, visualization, modeling, and applications.

Network graphs can show how your customers are segmented and the risk of churn for each segment. You can discover that what you thought were three or four main segments are actually 10.

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 ...

PyTorch has a unique way of building neural networks: using and replaying a tape recorder. Click here. NeuroLab. NeuroLab is a simple and powerful Neural Network Library for Python. This library ...