000			02102nam a2200349 i 4500
001			CR9781108924184
003			UkCbUP
005			20240906181338.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			200422s2021||||enk o ||1 0|eng|d
020			_a9781108924184 (ebook)
020			_z9781108831741 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050		4	_aQ325.5 _b.M3 2021
082	0	4	_a006.31 _223
100	1		_aMa, Yao, _eauthor.
245	1	0	_aDeep learning on graphs / _cYao Ma, Jiliang Tang.
264		1	_aCambridge : _bCambridge University Press, _c2021.
300			_a1 online resource (xviii, 320 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 07 Oct 2021).
520			_aDeep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
650		0	_aMachine learning.
650		0	_aGraph algorithms.
700	1		_aTang, Jiliang, _eauthor.
776	0	8	_iPrint version: _z9781108831741
856	4	0	_uhttps://doi.org/10.1017/9781108924184
942			_2ddc _cEB
999			_c9982 _d9982