Implement machine learning algorithms on network data features; Build and query graph databases; Explore new frontiers in network science such as quantum algorithms; Who this book is for. If you’re a ...

This book delves into graph-based algorithms in Python that tackle massive datasets. Using code examples, you’ll be able to leverage these techniques for big data analytics. This book covers the ...

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 paper, we propose a Graph Laplacian Pyramid Network (GLPN) for general imputation tasks, which follows the "draft-then-refine" procedures. Our model shows superior performance over ...

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.

Knowledge Graph is an ER-based (Entity-Relationship) feature representation learning approach that finds applications in various domains such as natural language processing, medical sciences, finance ...

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Whether you are a machine ...