Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch (Paperback)

Labonne, Maxime

  • 出版商: Packt Publishing
  • 出版日期: 2023-04-14
  • 售價: $1,800
  • 貴賓價: 9.5$1,710
  • 語言: 英文
  • 頁數: 354
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1804617520
  • ISBN-13: 9781804617526
  • 相關分類: Python程式語言DeepLearning
  • 立即出貨 (庫存=1)



Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps

Purchase of the print or Kindle book includes a free PDF eBook


Key Features:

  • Implement state-of-the-art graph neural network architectures in Python
  • Create your own graph datasets from tabular data
  • Build powerful traffic forecasting, recommender systems, and anomaly detection applications



Book Description:

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 have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.

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 network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.

By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.


What You Will Learn:

  • Understand the fundamental concepts of graph neural networks
  • Implement graph neural networks using Python and PyTorch Geometric
  • Classify nodes, graphs, and edges using millions of samples
  • Predict and generate realistic graph topologies
  • Combine heterogeneous sources to improve performance
  • Forecast future events using topological information
  • Apply graph neural networks to solve real-world problems


Who this book is for:

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.


使用PyTorch Geometric設計強大的圖形神經網絡,結合圖論和神經網絡的最新發展和應用。


- 使用Python實現最先進的圖形神經網絡架構
- 從表格數據創建自己的圖形數據集
- 構建強大的交通預測、推薦系統和異常檢測應用



通過閱讀本書,您將學習創建圖形數據集,使用Python和PyTorch Geometric實現圖形神經網絡,並應用它們解決現實世界的問題,包括構建和訓練用於節點和圖形分類、鏈接預測等的圖形神經網絡模型。

- 理解圖形神經網絡的基本概念
- 使用Python和PyTorch Geometric實現圖形神經網絡
- 使用數百萬個樣本對節點、圖形和邊進行分類
- 預測和生成真實的圖形拓撲
- 結合異構源以提高性能
- 使用拓撲信息預測未來事件
- 應用圖形神經網絡解決實際問題