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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031015885
024	7		_a10.1007/978-3-031-01588-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
100	1		_aHamilton, William L. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686640 _q(William Leif)
245	1	0	_aGraph Representation Learning _cby William L. Hamilton
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVII, 141 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aPreface -- 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 .
520			_aGraph-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.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9678664 _aRedes neuronales artificiales
650		7	_2embne _9146336 _aGrafos, Teoría de _xProceso de datos
776	0	8	_iPrinted edition: _z9783031000331
776	0	8	_iPrinted edition: _z9783031004605
776	0	8	_iPrinted edition: _z9783031027161
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01588-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI