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Graph Representation Learning, Hamilton William L.


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Автор: Hamilton William L.
Название:  Graph Representation Learning
ISBN: 9781681739656
Издательство: Mare Nostrum (Eurospan)
Классификация:


ISBN-10: 1681739658
Обложка/Формат: Hardback
Страницы: 159
Вес: 0.49 кг.
Дата издания: 30.09.2020
Серия: Synthesis lectures on artificial intelligence and machine learning
Язык: English
Размер: 23.50 x 19.05 x 1.42 cm
Читательская аудитория: Professional and scholarly
Ключевые слова: Artificial intelligence,Computer networking & communications,Neural networks & fuzzy systems, COMPUTERS / Intelligence (AI) & Semantics,COMPUTERS / Neural Networks,COMPUTERS / Web / Social Networking
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Поставляется из: Англии
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.



Graph Structures for Knowledge Representation and Reasoning

Автор: Madalina Croitoru; Sebastian Rudolph; Stefan Woltr
Название: Graph Structures for Knowledge Representation and Reasoning
ISBN: 3319045334 ISBN-13(EAN): 9783319045337
Издательство: Springer
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Цена: 58690.00 T
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Описание: This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2013, held in Beijing, China, in August 2013, associated with IJCAI 2013, the 23rd International Joint Conference on Artificial Intelligence.

Graph-based Knowledge Representation

Автор: Michel Chein; Marie-Laure Mugnier
Название: Graph-based Knowledge Representation
ISBN: 1849967695 ISBN-13(EAN): 9781849967693
Издательство: Springer
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Цена: 135090.00 T
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Описание: In addressing the question of how far it is possible to go in knowledge representation and reasoning through graphs, the authors cover basic conceptual graphs, computational aspects, and kernel extensions. The basic mathematical notions are summarized.

Graph Structures for Knowledge Representation and Reasoning

Автор: Croitoru
Название: Graph Structures for Knowledge Representation and Reasoning
ISBN: 3319781014 ISBN-13(EAN): 9783319781013
Издательство: Springer
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Цена: 39130.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2017, held in Melbourne, VIC, Australia, in August 2017, associated with IJCAI 2017, the 26th International Joint Conference on Artificial Intelligence.

Graph-Based Representation and Reasoning

Автор: Haemmerl?
Название: Graph-Based Representation and Reasoning
ISBN: 3319409840 ISBN-13(EAN): 9783319409849
Издательство: Springer
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Цена: 46590.00 T
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Описание: This book constitutes the proceedings of the 22th International Conference on Conceptual Structures, ICCS 2016, held in Annecy, France, in July 2016. The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 40 submissions.

Graph-Based Representation and Reasoning

Автор: Chapman
Название: Graph-Based Representation and Reasoning
ISBN: 3319913786 ISBN-13(EAN): 9783319913780
Издательство: Springer
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Цена: 46570.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the 23rd International Conference on Conceptual Structures, ICCS 2018, held in Edinburgh, UK, in June 2018. The 10 full papers, 2 short papers and 2 posters presented were carefully reviewed and selected from 21 submissions. computer- human interaction and human cognition;

Graph Structures for Knowledge Representation and Reasoning

Автор: Madalina Croitoru; Pierre Marquis; Sebastian Rudol
Название: Graph Structures for Knowledge Representation and Reasoning
ISBN: 331928701X ISBN-13(EAN): 9783319287010
Издательство: Springer
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Цена: 37270.00 T
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Описание: This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2015, held in Buenos Aires, Argentina, in July 2015, associated with IJCAI 2015, the 24th International Joint Conference on Artificial Intelligence.

Graph-Based Representation and Reasoning

Автор: Nathalie Hernandez; Robert J?schke; Madalina Croit
Название: Graph-Based Representation and Reasoning
ISBN: 3319083880 ISBN-13(EAN): 9783319083889
Издательство: Springer
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Цена: 68010.00 T
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Описание: This book constitutes the proceedings of the 21st International Conference on Conceptual Structures, ICCS 2014, held in Iasi, Romania, in July 2014.

Knowledge Representation for Agents and Multi-Agent Systems

Автор: John-Jules Meyer; Jan M. Broersen
Название: Knowledge Representation for Agents and Multi-Agent Systems
ISBN: 3642053009 ISBN-13(EAN): 9783642053009
Издательство: Springer
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Цена: 65210.00 T
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Описание: First International Workshop KRAMAS 2008 Sydney Australia September 17 2008 Revised Selected Papers. .

Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation

Автор: Thomas Eiter; Hannes Strass; Miros?aw Truszczy?ski
Название: Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation
ISBN: 3319147250 ISBN-13(EAN): 9783319147253
Издательство: Springer
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Цена: 52170.00 T
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Описание: This Festschrift is published in honor of Gerhard Brewka on the occasion of his 60th birthday and contains articles from fields reflecting the breadth of Gerd`s work.

Diagrammatic Representation and Inference

Автор: Tim Dwyer; Helen Purchase; Aidan Delaney
Название: Diagrammatic Representation and Inference
ISBN: 3662440423 ISBN-13(EAN): 9783662440421
Издательство: Springer
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Цена: 52170.00 T
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Описание: This book constitutes the refereed proceedings of the 8th International Conference on the Theory and Application of Diagrams, Diagrams 2014, held in Melbourne, VIC, Australia in July/August 2014.

Process Support and Knowledge Representation in Health Care

Автор: David Riano; Richard Lenz; Silvia Miksch; Mor Pele
Название: Process Support and Knowledge Representation in Health Care
ISBN: 3319039156 ISBN-13(EAN): 9783319039152
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book constitutes the thoroughly refereed papers from the BPM 2013 Joint Workshop on Process-Oriented Information Systems and Knowledge Representation in Health Care, KR4HC 2013/ProHealth 2013, held in Murcia, Spain, in June 2013.

Graph Representation Learning

Автор: Hamilton William L.
Название: Graph Representation Learning
ISBN: 1681739631 ISBN-13(EAN): 9781681739632
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 57290.00 T
Наличие на складе: Невозможна поставка.
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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