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Condition: Good. Ships SAME or NEXT business day. We Ship to APO/FPO addr. Choose EXPEDITED shipping and receive in 2-5 business days within the United States. See our member profile for customer support contact info. We have an easy return policy.
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Graph Neural Networks: Foundations, Frontiers, and Applications
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Graph Neural Networks: Foundations, Frontiers, and Applications
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Condition: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
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Graph Neural Networks: Foundations, Frontiers, and Applications
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Graph Neural Networks: Foundations, Frontiers, And Applications
Published by Springer, 2022
Language: English
Soft cover. Condition: New. ISBN:9789811660566.
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Graph Neural Networks : Foundations, Frontiers, and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Graph Neural Networks : Foundations, Frontiers, and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Graph Neural Networks: Foundations, Fron
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Graph Neural Networks: Foundations, Frontiers, and Applications
Published by Springer Verlag, Singapore, SG, 2023
ISBN 10: 9811660565 ISBN 13: 9789811660566
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Paperback. Condition: New. 2022 ed. Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history,current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
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Graph Neural Networks: Foundations, Frontiers, and Applications
Published by Springer Verlag, Singapore, 2022
ISBN 10: 9811660530 ISBN 13: 9789811660535
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Graph Neural Networks: Foundations, Frontiers, and Applications
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Graph Neural Networks: Foundations, Frontiers, and Applications (Hardcover)
Published by Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811660530 ISBN 13: 9789811660535
Language: English
Hardcover. Condition: new. Hardcover. Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history,current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Graph Neural Networks : Foundations, Frontiers, and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New.
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Graph Neural Networks : Foundations, Frontiers, and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Graph Neural Networks: Foundations, Frontiers, and Applications
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9811660565 ISBN 13: 9789811660566
Language: English
Condition: New. Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data suc.
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Taschenbuch. Condition: Neu. Graph Neural Networks: Foundations, Frontiers, and Applications | Lingfei Wu (u. a.) | Taschenbuch | xxxvi | Englisch | 2023 | Springer | EAN 9789811660566 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
