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020			_a9789813340220
024	7		_a10.1007/978-981-33-4022-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aAggarwal, Manasvi _eautor _4http://id.loc.gov/vocabulary/relators/aut _9678612
245	1	0	_aMachine Learning in Social Networks : _bEmbedding Nodes, Edges, Communities, and Graphs _cby Manasvi Aggarwal, MN Murty
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XI, 112 páginas) _b29 ilustraciones, 18 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computational Intelligence _x2625-3704
505	0		_aIntroduction -- Representations of Networks -- Deep Learning -- Node Representations -- Embedding Graphs -- Conclusions.
520	3		_aThis book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein-protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.
988			_aSpringer_Computer_2021
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aMurty, M. Narasimha _eautor _4http://id.loc.gov/vocabulary/relators/aut _999777
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9789813340213
776	0	8	_iPrinted edition: _z9789813340237
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-4022-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _ek _zSI