Graph Representation Learning
- Machine Learning
- Categories:Computers & Internet
- Language:English(Translation Services Available)
- Publication date:September,2020
- Pages:159
- Retail Price:(Unknown)
- Size:190mm×234mm
- Page Views:211
- Words:(Unknown)
- Star Ratings:
- Text Color:Black and white
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Feature
★ Written by William L. Hamilton, a Canada CIFAR Chair in AI, whose 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.
★ An authoritative book on graph representation learning!
Description
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphsa nascent but quickly growing subset of graph representation learning.