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008			220601s2020 sz | o |||| 0|eng d
020			_a9783031015878
024	7		_a10.1007/978-3-031-01587-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2020 EB
100	1		_aLiu, Zhiyuan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9682283
245	1	0	_aIntroduction to Graph Neural Networks _cby Zhiyuan Liu, Jie Zhou
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVII, 109 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 -- Basics of Math and Graph -- Basics of Neural Networks -- Vanilla Graph Neural Networks -- Graph Convolutional Networks -- Graph Recurrent Networks -- Graph Attention Networks -- Graph Residual Networks -- Variants for Different Graph Types -- Variants for Advanced Training Methods -- General Frameworks -- Applications -- Structural Scenarios -- Applications -- Non-Structural Scenarios -- Applications -- Other Scenarios -- Open Resources -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aGraphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9678664 _aRedes neuronales artificiales
650		7	_2embne _9146336 _aGrafos, Teoría de
700	1		_aZhou, Jie _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9101029
776	0	8	_iPrinted edition: _z9783031000324
776	0	8	_iPrinted edition: _z9783031004599
776	0	8	_iPrinted edition: _z9783031027154
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01587-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI