Table of Contents

Preface.- Acknowledgments.- Introduction.- Background and Traditional Approaches.- Neighborhood Reconstruction Methods.- Multi-Relational Data and Knowledge Graphs.- The Graph Neural Network Model.- Graph Neural Networks in Practice.- Theoretical Motivations.- Traditional Graph Generation Approaches.- Deep Generative Models.- Conclusion.- Bibliography.- Author's Biography .

About the Author

William L. Hamilton is an Assistant Professor of Computer Science at McGill University and a Canada CIFAR Chair in AI. His research focuses on graph representation learning as well as applications in computational social science and biology. In recent years, he has published more than 20 papers on graph representation learning at top-tier venues across machine learning and network science, as well as co-organized several large workshops and tutorials on the topic. Williams work has been recognized by several awards, including the 2018 Arthur L. Samuel Thesis Award for the best doctoral thesis in the Computer Science department at Stanford University and the 2017 Cozzarelli Best Paper Award from the Proceedings of the National Academy of Sciences.