This book covers comprehensive contents in developing deep learning techniques for graph structured data with a specific focus on Graph Neural Networks (GNNs). The foundation of the GNN models are introduced in detail including the two main building operations: graph filtering and pooling operations. We then discuss the robustness and scalability of the GNNs, which are extremely important for utilizing GNNs for real-world applications. To enable deep learning techniques to advance more graph tasks under wider settings, we introduce numerous deep graph models beyond GNNs. We also present the most representative applications of GNNs in different areas such as Natural Language Processing, Computer Vision, Data Mining and Healthcare. The book is also self-contained, we include chapters for introducing some basics on graphs and also on deep learning. We conclude the book with recent advances of GNNs in both methods and applications.
The whole book has been submitted to the Cambridge Press at the end of July. The book should be officially available on September as well as the Chinese
version with Publishing House of Electronic Industry (电子工业出版社).
English Version: Yao Ma and Jiliang Tang
Chinese Version: Yiqi Wang, Wei Jin, Yao Ma and Jiliang Tang
This material will be published by Cambridge University Press as Deep Learning on Graphs by Yao Ma and Jiliang Tang. This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © Yao Ma and Jiliang Tang 2020
Chapter 1 Deep Learning on Graphs: An Introduction [Introduction] [preprint]
Chapter 2 Foundations of Graphs [Introduction] [preprint]
Chapter 3 Foundations of Deep Learning [Introduction] [preprint]
Chapter 4 Graph Embedding [Introduction] [preprint]
Chapter 5 Graph Neural Networks [Introduction] [preprint]
Chapter 6 Robust Graph Neural Networks [Introduction] [preprint]
Chapter 7 Scalable Graph Neural Networks [Introduction] [preprint]
Chapter 8 Graph Neural Networks on Complex Graphs [Introduction] [preprint]
Chapter 9 Beyond GNNs: More Deep Models on Graphs [Introduction] [preprint]
Chapter 10 Graph Neural Networks in Natural Language Processing [Introduction] [preprint]
Chapter 11 Graph Neural Networks in Computer Vision [Introduction] [preprint]
Chapter 12 Graph Neural Networks in Data Mining [Introduction] [preprint]
Chapter 13 Graph Neural Networks in Biochemistry and Healthcare [Introduction] [preprint]
Chapter 14 Advanced Topics in Graph Neural Networks [Introduction] [preprint]
Chapter 15 Advanced Applications in Graph Neural Networks [Introduction] [preprint]
Thanks for Prakash Upadhyay for merging all chapters. He also designed a nice cover for the book. Thanks! The whole book can be found via his Github.
Any comments and feedback are welcomed and appreciated! Please send your comments and feedback to mayao4 at msu dot edu
To cite the book, please use the followng bibtex entry:
@book{ma2020deep,
title={Deep Learning on Graphs},
author={Yao Ma and Jiliang Tang},
publisher={Cambridge University Press},
year={2020}
}