Deep learning on graphs
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 meth...
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Cambridge University Press
2021
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| 100 | 1 | |a Ma, Yao | |
| 245 | 1 | 0 | |a Deep learning on graphs |c Yao Ma, Jiliang Tang |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2021 | |
| 300 | |a 1 Online-Ressource (xviii, 320 Seiten) | ||
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| 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. | ||
| 700 | 1 | |a Tang, Jiliang | |
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Datensatz im Suchindex
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| id | ZDB-20-CTM-CR9781108924184 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T12:04:27Z |
| institution | BVB |
| isbn | 9781108924184 |
| language | English |
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| publishDate | 2021 |
| publishDateSearch | 2021 |
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| publisher | Cambridge University Press |
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| spelling | Ma, Yao Deep learning on graphs Yao Ma, Jiliang Tang Cambridge Cambridge University Press 2021 1 Online-Ressource (xviii, 320 Seiten) txt c cr 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. Tang, Jiliang Erscheint auch als Druck-Ausgabe 9781108831741 TUM01 ZDB-20-CTM TUM_PDA_CTM https://doi.org/10.1017/9781108924184 Volltext |
| spellingShingle | Ma, Yao Deep learning on graphs |
| title | Deep learning on graphs |
| title_auth | Deep learning on graphs |
| title_exact_search | Deep learning on graphs |
| title_full | Deep learning on graphs Yao Ma, Jiliang Tang |
| title_fullStr | Deep learning on graphs Yao Ma, Jiliang Tang |
| title_full_unstemmed | Deep learning on graphs Yao Ma, Jiliang Tang |
| title_short | Deep learning on graphs |
| title_sort | deep learning on graphs |
| url | https://doi.org/10.1017/9781108924184 |
| work_keys_str_mv | AT mayao deeplearningongraphs AT tangjiliang deeplearningongraphs |