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.
9781108924184 (ebook)
Machine learning.
Graph algorithms.
Q325.5 / .M3 2021
006.31
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.
9781108924184 (ebook)
Machine learning.
Graph algorithms.
Q325.5 / .M3 2021
006.31
