Graph Representation Learning / by William L. Hamilton.

Format:

Book

Author/Creator:

Hamilton, William L., Author.

Series:

Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Neural networks (Computer science).

Artificial Intelligence.

Machine Learning.

Mathematical Models of Cognitive Processes and Neural Networks.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Mathematical Models of Cognitive Processes and Neural Networks.

Physical Description:

1 online resource (XVII, 141 p.)

Edition:

1st ed. 2020.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2020.

Summary:

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. 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 graphs—a nascent but quickly growing subset of graph representation learning.

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 .

ISBN:

9783031015885

3031015886