You're reading from Hands-On Graph Neural Networks Using Python

Product type Book

Published in Apr 2023

Publisher Packt

ISBN-13 9781804617526

Pages 354 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Maxime Labonne

Preface

Part 1: Introduction to Graph Learning

Chapter 1: Getting Started with Graph Learning

Chapter 2: Graph Theory for Graph Neural Networks

Chapter 3: Creating Node Representations with DeepWalk

Part 2: Fundamentals

Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec

Chapter 5: Including Node Features with Vanilla Neural Networks

Chapter 6: Introducing Graph Convolutional Networks

Chapter 7: Graph Attention Networks

Part 3: Advanced Techniques

Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE

Chapter 9: Defining Expressiveness for Graph Classification

Chapter 10: Predicting Links with Graph Neural Networks

Chapter 11: Generating Graphs Using Graph Neural Networks

Chapter 12: Learning from Heterogeneous Graphs

Chapter 13: Temporal Graph Neural Networks

Chapter 14: Explaining Graph Neural Networks

Part 4: Applications

Chapter 15: Forecasting Traffic Using A3T-GCN

Chapter 16: Detecting Anomalies Using Heterogeneous GNNs

Chapter 17: Building a Recommender System Using LightGCN

Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications

Index

Other Books You May Enjoy

[1] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl. Neural Message Passing for Quantum Chemistry. arXiv, 2017. DOI: 10.48550/ARXIV.1704.01212. Available: https://arxiv.org/abs/1704.01212.
[2] Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of Academic Social Networks. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’2008). pp.990–998. Available: https://dl.acm.org/doi/abs/10.1145/1401890.1402008.
[3] X. Fu, J. Zhang, Z. Meng, and I. King. MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. Apr. 2020. DOI: 10.1145/3366423.3380297. Available: https://arxiv.org/abs/2002.01680.
[4] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling. Modeling Relational Data with Graph Convolutional Networks. arXiv, 2017. DOI: 10.48550/ARXIV.1703.06103. Available...