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ISBN 10: 7111711386 ISBN 13: 9787111711384
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paperback. Condition: 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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ISBN 10: 9811661685 ISBN 13: 9789811661686
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Published by Machinery Industry Press, 2022
ISBN 10: 7111711386 ISBN 13: 9787111711384
Language: Chinese
Seller: liu xing, Nanjing, JS, China
paperback. Condition: 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.
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811661685 ISBN 13: 9789811661686
Language: English
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Add to basketKartoniert / Broschiert. Condition: 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.
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2022
ISBN 10: 9811661650 ISBN 13: 9789811661655
Language: English
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Add to basketGebunden. Condition: 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.
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