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020			_a9789811661662
024	7		_a10.1007/978-981-16-6166-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.5913 _b2022 EB
100	1		_aShi, Chuan _eautor _4http://id.loc.gov/vocabulary/relators/aut _9683251
245	1	0	_aHeterogeneous Graph Representation Learning and Applications _cby Chuan Shi, Xiao Wang, Philip S. Yu
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XX, 318 páginas) _b1 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aArtificial Intelligence: Foundations Theory and Algorithms _x2365-306X
505	0		_aIntroduction -- The State-of-the-art of Heterogeneous Graph Representation -- Part One: Techniques -- Structure-preserved Heterogeneous Graph Representation -- Attribute-assisted Heterogeneous Graph Representation -- Dynamic Heterogeneous Graph Representation -- Supplementary of Heterogeneous Graph Representation -- Part Two: Applications -- Heterogeneous Graph Representation for Recommendation -- Heterogeneous Graph Representation for Text Mining -- Heterogeneous Graph Representation for Industry Application -- Future Research Directions -- Conclusion.
520			_aRepresentation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
988			_aSpringer_Computer_2022
650		7	_2embne _9543538 _aRepresentación del conocimiento
700	1		_aWang, Xiao _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aYu, Philip S _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811661655
776	0	8	_iPrinted edition: _z9789811661679
776	0	8	_iPrinted edition: _z9789811661686
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-6166-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eh _zSI