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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,
Description:	1 online resource (xviii, 320 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 07 Oct 2021).
Subject:	Graph algorithms. -
Online resource:	https://doi.org/10.1017/9781108924184
ISBN:	9781108924184 (ebook)

Deep learning on graphs /
Ma, Yao,

Deep learning on graphs /Yao Ma, Jiliang Tang. - 1 online resource (xviii, 320 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 07 Oct 2021).

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: 9781108924184 (ebook)Subjects--Topical Terms:

713780
Graph algorithms.

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

Dewey Class. No.: 006.31

Deep learning on graphs /
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245 1 0 $a Deep learning on graphs / $c Yao Ma, Jiliang Tang.
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300 $a 1 online resource (xviii, 320 pages) : $b digital, PDF file(s).
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500 $a Title from publisher's bibliographic system (viewed on 07 Oct 2021).
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 Graph algorithms. $3 713780
650 0 $a Machine learning. $3 561253
700 1 $a Tang, Jiliang, $e author. $3 1445686
776 0 8 $i Print version: $z 9781108831741
856 4 0 $u https://doi.org/10.1017/9781108924184

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