Lingfei Wu (40 results)
Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Softcover
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Softcover
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Softcover
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- Softcover
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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.
Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Hardcover
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- Softcover
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- Hardcover
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Hardcover
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Hardcover
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- Hardcover
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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.
- Softcover
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Graph Neural Networks : Foundations, Frontiers, and Applications
Wu, Lingfei (EDT); Cui, Peng (EDT); Pei, Jian (EDT); Zhao, Liang (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
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- Softcover
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More images- Softcover
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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 | Anbiet…er: preigu.
- Softcover
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Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Editor) / Cui, Peng (Editor) / Pei, Jian (Editor) / Zhao, Liang (Editor)
- Softcover
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Paperback. Condition: Brand New. 725 pages. 9.25x6.10x1.73 inches. In Stock.
- Softcover
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Paperback. Condition: New. 2022 ed.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 da…ta 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.
- Hardcover
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- Hardcover
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Edited by)/ Cui, Peng (Edited by)/ Pei, Jian (Edited by)/ Zhao, Liang (Edited by)
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 725 pages. 9.25x6.10x1.54 inches. In Stock.
- Hardcover
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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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
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- Softcover
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Jan 2023, 2023
- Softcover
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 gra…ph-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. 728 pp. Englisch.
- Hardcover
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