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Deep learning on graphs

[NT 42944] Record Type:	[NT 1579] Language materials, printed : [NT 40817] monographic
[NT 47261] Author:	MaYao,
[NT 47353] Alternative Intellectual Responsibility:	TangJiliang,
[NT 47351] Place of Publication:	Cambridge, UK
[NT 47263] Published:	Cambridge University Press;
[NT 47352] Year of Publication:	2021
[NT 47264] Description:	xviii, 320 p.ill. : 24 cm.;
[NT 47266] Subject:	Graph algorithms. -
[NT 47266] Subject:	Machine learning. -
[NT 51398] Summary:	Deep 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.
[NT 50961] ISBN:	978-1-108-83174-1bound
[NT 50961] ISBN:	1-108-83174-5bound

Deep learning on graphs
Ma, Yao

Deep learning on graphs / Yao Ma, Jiliang Tang - Cambridge, UK : Cambridge University Press, 2021. - xviii, 320 p. ; ill. ; 24 cm..
Includes bibliographical references (p. 289-314) and index..
ISBN 978-1-108-83174-1ISBN 1-108-83174-5
Graph algorithms.Machine learning.

Tang, Jiliang

Deep learning on graphs
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200 1 $a Deep learning on graphs $f Yao Ma, Jiliang Tang
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215 1 $a xviii, 320 p. $c ill. $d 24 cm.
320 $a Includes bibliographical references (p. 289-314) and index.
330 $a Deep 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.
606 # $a Graph algorithms. $3 220981
606 # $a Machine learning. $3 126290
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701 0 $a Tang $b Jiliang $3 419278
801 0 $a tw $b 嶺東科技大學圖書館