Description: Further DetailsTitle: Graph Representation LearningCondition: NewEAN: 9783031004605ISBN: 9783031004605Publisher: Springer International Publishing AGFormat: PaperbackRelease Date: 09/16/2020Description: 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. Language: EnglishCountry/Region of Manufacture: CHItem Height: 235mmItem Length: 191mmAuthor: William L. HamiltonGenre: Science Nature & MathBook Series: Synthesis Lectures on Artificial Intelligence and Machine LearningTopic: Computing & InternetRelease Year: 2020 Missing Information?Please contact us if any details are missing and where possible we will add the information to our listing.
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Book Title: Graph Representation Learning
Title: Graph Representation Learning
EAN: 9783031004605
ISBN: 9783031004605
Release Date: 09/16/2020
Release Year: 2020
Country/Region of Manufacture: CH
Item Height: 235mm
Genre: Science Nature & Math
Topic: Computing & Internet
Number of Pages: Xvii, 141 Pages
Language: English
Publication Name: Graph Representation Learning
Publisher: Springer International Publishing A&G
Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Applied
Publication Year: 2020
Item Weight: 11.1 Oz
Type: Textbook
Author: William L. Hamilton
Item Length: 9.3 in
Subject Area: Mathematics, Computers
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning Ser.
Item Width: 7.5 in
Format: Trade Paperback