Da: liu xing, Nanjing, JS, Cina
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Aggiungi al carrellopaperback. Condizione: New. Language:Chinese.Paperback.Pub Date:2022-08-01 Pages:259 Publisher:Machinery Industry Press This book aims to comprehensively review the development of heterogeneous graph representation learning and introduce its latest research progress. The book first summarizes the existing work from both the methodological and technical perspectives. and introduces some open resources in this field. The categories then detail the latest models and applications. It concludes with a discussion of future re.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Seiten: 340 | Sprache: Englisch | Produktart: Bücher | Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Da: Revaluation Books, Exeter, Regno Unito
EUR 247,54
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Aggiungi al carrelloPaperback. Condizione: Brand New. 338 pages. 9.25x6.10x0.79 inches. In Stock.
Da: liu xing, Nanjing, JS, Cina
EUR 142,27
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Aggiungi al carrellopaperback. Condizione: New. Language:Chinese.Paperback.Pub Date:2022-08-01 Pages:259 Publisher:Machinery Industry Press This book aims to comprehensively review the development of heterogeneous graph representation learning and introduce its latest research progress. The book first summarizes the existing work from both the methodological and technical perspectives. and introduces some open resources in this field. The categories then detail the latest models and applications. It concludes with a discussion of future re.
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811661685 ISBN 13: 9789811661686
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 144,94
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This tas.
Editore: Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811661650 ISBN 13: 9789811661655
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 144,94
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This tas.
Da: Majestic Books, Hounslow, Regno Unito
EUR 220,17
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: Majestic Books, Hounslow, Regno Unito
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 236,12
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