Graph Neural Networks Foundations (45 results)
Language: English
Published by Vibrant Publishers 8/8/2026, 2026
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Paperback or Softback. Condition: New. Graph Machine Learning Essentials: Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases. Book.
Language: English
Published by Vibrant Publishers, 2026
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Language: English
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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)
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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)
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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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Language: English
Published by Vibrant Publishers Aug 2026, 2026
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Taschenbuch. Condition: Neu. Neuware - What if the most important information in your data lies not in individual rows and columns, but in the connections between them Graph machine learning helps uncover patterns hidden in these relationships.Graph Machine Learning Essentials is a practical and accessible guide to understanding… how machine learning works with graph-structured data, where entities are connected through relationships.Designed for software engineers, ML engineers, data scientists, research scholars, professionals, cybersecurity analysts, and students, the book introduces graph machine learning in a clear and structured way. It begins with the fundamentals of graph theory and moves into core graph learning tasks such as node classification, edge prediction, and graph classification. Readers learn how graphs are represented in data structures, how node and edge embeddings work, and why traditional machine learning approaches do not directly apply to graph data.The book gradually builds toward graph neural networks, message passing, and advanced GNN architectures while explaining practical challenges such as graph construction, scalability, oversmoothing, and over-squashing. Concepts are connected to real-world applications across domains such as recommender systems, fraud detection, cybersecurity, bioinformatics, transportation networks, and knowledge graphs.The book includes two helpful appendices-one reviewing essential machine learning concepts and the other introducing PyTorch Geometric to help readers get started quickly.After reading this book, you will be able to: - Understand key graph machine learning concepts and terminology- Implement graph neural networks using PyTorch Geometric- Work on real-world graph learning problems across industries- Handle practical challenges such as large graphs and oversmoothing.
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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)
- 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.
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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Language: English
Published by Vibrant Publishers Aug 2026, 2026
- Hardcover
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Buch. Condition: Neu. Neuware - What if the most important information in your data lies not in individual rows and columns, but in the connections between them Graph machine learning helps uncover patterns hidden in these relationships.Graph Machine Learning Essentials is a practical and accessible guide to understanding how ma…chine learning works with graph-structured data, where entities are connected through relationships.Designed for software engineers, ML engineers, data scientists, research scholars, professionals, cybersecurity analysts, and students, the book introduces graph machine learning in a clear and structured way. It begins with the fundamentals of graph theory and moves into core graph learning tasks such as node classification, edge prediction, and graph classification. Readers learn how graphs are represented in data structures, how node and edge embeddings work, and why traditional machine learning approaches do not directly apply to graph data.The book gradually builds toward graph neural networks, message passing, and advanced GNN architectures while explaining practical challenges such as graph construction, scalability, oversmoothing, and over-squashing. Concepts are connected to real-world applications across domains such as recommender systems, fraud detection, cybersecurity, bioinformatics, transportation networks, and knowledge graphs.The book includes two helpful appendices-one reviewing essential machine learning concepts and the other introducing PyTorch Geometric to help readers get started quickly.After reading this book, you will be able to: - Understand key graph machine learning concepts and terminology- Implement graph neural networks using PyTorch Geometric- Work on real-world graph learning problems across industries- Handle practical challenges such as large graphs and oversmoothing.
- 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.
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Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Editor) / Cui, Peng (Editor) / Pei, Jian (Editor) / Zhao, Liang (Editor)
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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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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.
Graph Neural Networks: Foundations, Frontiers, and Applications
Wu, Lingfei (Edited by)/ Cui, Peng (Edited by)/ Pei, Jian (Edited by)/ Zhao, Liang (Edited by)
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Hardcover. Condition: Brand New. 725 pages. 9.25x6.10x1.54 inches. In Stock.
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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.
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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.
