MARC details
| 000 -LEADER |
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| fixed length control field |
01769 a2200229 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220224b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781681739632 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
HAM |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hamilton, William L. |
| 245 ## - TITLE STATEMENT |
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| Title |
Graph representation learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Morgan and Claypool Publishers, |
| Date of publication, distribution, etc |
2020. |
| Place of publication, distribution, etc |
California: |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 141p.; |
| Other physical details |
pbk; |
| Dimensions |
24cm |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
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| Title |
Synthesis lectures on artificial intelligence and machine learning |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
includes bibliography |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism. 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.<br/><br/>https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1576 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Neural networks |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Graph theory--Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Deep Generative Models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Graph-Structured data |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Books |
