Introduction to the Grape-Book Project
The Grape-Book project is an extensive tutorial designed to provide an accessible introduction to graph deep learning. It draws from the work of notable authors and institutions including JD’s “Graph Deep Learning: From Theory to Practice”, Ji-liang Tang’s team at Michigan State University, and Stanford University’s CS224W course on graph machine learning. The aim is to make the subject of graph deep learning comprehensible and approachable to readers, combining theoretical foundations with practical applications.
Project Overview
The tutorial offers an integration of deep learning fundamentals, the principles of graph theory, and traditional graph neural network (GNN) models. It goes beyond theory by providing hands-on codes through open-source graph frameworks such as NetworkX, DGL (Deep Graph Library), and PyG (PyTorch Geometric), enabling learners to directly implement and practice the concepts taught.
Content Structure
The Grape-Book is structured into chapters, each focusing on a key aspect of graph deep learning. Below is a brief overview:
- Chapter 1: Introduction - An overview of what graph deep learning entails, setting the pace for ensuing topics.
- Chapter 2: Basic Graph Theory - Covers the foundational graph theory concepts necessary for understanding graph structures and operations.
- Chapter 3: Fundamentals of Deep Learning - Introduces the basics of deep learning, which are essential for comprehending GNNs.
- Chapter 4: Graph Representation Learning - Focuses on techniques for learning graph representations, transforming graph data into a format digestible by machine learning models.
- Chapter 5: Graph Convolutional Networks (GCNs) - Delves into graph convolution techniques and their application in neural networks.
- Chapter 6: Relational Graph Convolutional Neural Networks - Enhances understanding of how GCNs can be extended to relational data.
- Chapter 7: Graph Attention Networks - Explores networks that use attention mechanisms to process graph data more effectively.
Future chapters are anticipated to dive into real-world applications of GNNs in various fields, further bridging the gap between academic theory and industry practice.
Contributors
The Grape-Book project is powered by a team of passionate contributors:
- @小饭 - Project lead, responsible for Chapters 1, 2, and 4, as well as proofreading.
- @银晗 - Authored Chapter 3 on deep learning basics.
- @洋 - Developed Chapter 5 on Graph Convolutional Networks.
- @汝超 - Initiated the project and penned Chapter 6 on Relational Graph Convolution Networks.
- @凯 - Wrote Chapter 7 on Graph Attention Networks.
How to Contribute
The project is open for contributions. Interested individuals can open an issue to propose new chapters or suggest improvements. Completed contributions can be made through pull requests. Bugs and suggestions are welcome via the issue tracker. For more involvement, prospective contributors can contact the project lead @小饭 on GitHub.
Acknowledgments
The project received valuable support from Dr. Yao, author of “Graph Deep Learning: From Theory to Practice”. Interested readers can purchase the book through JD.com for deeper understanding.
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