Graph representation learning

著者

• Hamilton, William L.

書誌事項

Graph representation learning

William L. Hamilton

（Synthesis lectures on artificial intelligence and machine learning, #46）

Morgan & Claypool, c2020

• : pbk

注記

Includes bibliographical references (p. 131-140)

内容説明・目次

内容説明

This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism. 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. 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.

目次

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

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BC09271978
• ISBN:
  • 9781681739632
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: [San Rafael, Calif.]
• ページ数/冊数: xvii, 141 p.
• 大きさ: 24 cm
• 親書誌ID:
  • BA85080963