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Deep learning on graphs /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning on graphs // Yao Ma, Jiliang Tang.
Author:	Ma, Yao.
other author:	Tang, Jiliang.
Published:	Cambridge, UK :Cambridge University Press, : c2021.,
Description:	xviii, 320 p. :ill. ; : 24 cm.;
Subject:	Machine learning. -
ISBN:	9781108831741 :

Deep learning on graphs /
Ma, Yao.

Deep learning on graphs /Yao Ma, Jiliang Tang. - Cambridge, UK :Cambridge University Press,c2021. - xviii, 320 p. :ill. ;24 cm.

Includes bibliographical references (p. 289-314) and index.

Deep learning on graphs : an introduction -- Part I : Foundations. Foundations on graphs -- Foundations of deep learning -- Part II : Methods. Graph embedding -- Graph neural networks -- Robust graph neural networks -- Scalable graph neural networks -- Graph neural networks for complex graphs -- Beyond GNNs : more deep models on graphs -- Part III : Applications. Graph neural networks in natural language processing -- Graph neural networks in computer vision -- Graph neural networks in data mining -- Graph neural networks in biochemistry and health care -- Part IV : Advances. Advanced topics in graph neural networks -- Advanced applications in graph neural networks.

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.

ISBN: 9781108831741 :NT1555

Nat. Bib. No.: GBC1C5112bnbSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .M3 2021

Dewey Class. No.: 006.31

Deep learning on graphs /
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020 $a 9781108831741 : $c NT1555
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100 1 $a Ma, Yao. $3 1195628
245 1 0 $a Deep learning on graphs / $c Yao Ma, Jiliang Tang.
260 # $a Cambridge, UK : $b Cambridge University Press, $c c2021.
300 $a xviii, 320 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references (p. 289-314) and index.
505 0 # $a Deep learning on graphs : an introduction -- Part I : Foundations. Foundations on graphs -- Foundations of deep learning -- Part II : Methods. Graph embedding -- Graph neural networks -- Robust graph neural networks -- Scalable graph neural networks -- Graph neural networks for complex graphs -- Beyond GNNs : more deep models on graphs -- Part III : Applications. Graph neural networks in natural language processing -- Graph neural networks in computer vision -- Graph neural networks in data mining -- Graph neural networks in biochemistry and health care -- Part IV : Advances. Advanced topics in graph neural networks -- Advanced applications in graph neural networks.
520 # $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.
650 # 0 $a Machine learning. $3 561253
650 # 0 $a Graph algorithms. $3 713780
700 1 # $a Tang, Jiliang. $3 1372868