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...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
[2021]
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 TUM01 UBM01 UER01 UPA01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (xviii, 320 Seiten) |
| ISBN: | 9781108924184 |
| DOI: | 10.1017/9781108924184 |
Internformat
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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 | ||
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Datensatz im Suchindex
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| author | Ma, Yao ca. 20./21. Jh Tang, Jiliang ca. 20./21. Jh |
| author_GND | (DE-588)1244840009 (DE-588)1142538648 |
| author_facet | Ma, Yao ca. 20./21. Jh Tang, Jiliang ca. 20./21. Jh |
| author_role | aut aut |
| author_sort | Ma, Yao ca. 20./21. Jh |
| author_variant | y m ym j t jt |
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| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.1017/9781108924184 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:29:36Z |
| indexdate | 2024-07-10T09:15:06Z |
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| isbn | 9781108924184 |
| language | English |
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| spelling | Ma, Yao ca. 20./21. Jh. Verfasser (DE-588)1244840009 aut Deep learning on graphs Yao Ma, Jiliang Tang Cambridge Cambridge University Press [2021] © 2021 1 Online-Ressource (xviii, 320 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 Machine learning Graph algorithms Tang, Jiliang ca. 20./21. Jh. Verfasser (DE-588)1142538648 aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-108-83174-1 https://doi.org/10.1017/9781108924184 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Ma, Yao ca. 20./21. Jh Tang, Jiliang ca. 20./21. Jh Deep learning on graphs Machine learning Graph algorithms |
| title | Deep learning on graphs |
| title_auth | Deep learning on graphs |
| title_exact_search | Deep learning on graphs |
| title_exact_search_txtP | 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 |
| topic | Machine learning Graph algorithms |
| topic_facet | Machine learning Graph algorithms |
| url | https://doi.org/10.1017/9781108924184 |
| work_keys_str_mv | AT mayao deeplearningongraphs AT tangjiliang deeplearningongraphs |