Graph Representation Learning

Name: Graph Representation Learning
Brand: Springer International Publishing AG
SKU: 9783031004605
Price: 35.72 EUR
Availability: InStock

Автор: Hamilton, William L.

Език:

Корици: С меки корици

Издател: Springer International Publishing AG

Наличност: Външен склад

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35.72 € 69.87 лв

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunicati...

Информация за книгата

Автор

Hamilton, William L.

Език

Английски език

Корици

Книга - С меки корици

Издадена

2020

страници

141

EAN

9783031004605

ISBN

3031004604

Enbook ID

39298080

Издател

Springer International Publishing AG

Теглоt

322

Размери

234 x 190 x 14

Пълно описание

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.

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