Graph Representation Learning (Record no. 84820)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03916nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01588-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163632.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2020 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031015885 |
| -- | 978-3-031-01588-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Hamilton, William L. |
| 245 10 - TITLE STATEMENT | |
| Title | Graph Representation Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XVII, 141 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Artificial Intelligence and Machine Learning, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface -- Acknowledgments -- Introduction -- Background and Traditional Approaches -- Neighborhood Reconstruction Methods -- Multi-Relational Data and Knowledge Graphs -- The Graph Neural Network Model -- Graph Neural Networks in Practice -- Theoretical Motivations -- Traditional Graph Generation Approaches -- Deep Generative Models -- Conclusion -- Bibliography -- Author's Biography . |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing subset of graph representation learning. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01588-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2020. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) . |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical Models of Cognitive Processes and Neural Networks. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1939-4616 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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