東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Graph neural networks = foundations,...

Wu, Lingfei.

Linked to FindBook

Google Book

Amazon

博客來

Graph neural networks = foundations, frontiers, and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Graph neural networks/ edited by Lingfei Wu ... [et al.].
Reminder of title:	foundations, frontiers, and applications /
other author:	Wu, Lingfei.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	xxxvi, 689 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Representation Learning -- Chapter 2. Graph Representation Learning -- Chapter 3. Graph Neural Networks -- Chapter 4. Graph Neural Networks for Node Classification -- Chapter 5. The Expressive Power of Graph Neural Networks -- Chapter 6. Graph Neural Networks: Scalability -- Chapter 7. Interpretability in Graph Neural Networks -- Chapter 8. "Graph Neural Networks: Adversarial Robustness" -- Chapter 9. Graph Neural Networks: Graph Classification -- Chapter 10. Graph Neural Networks: Link Prediction -- Chapter 11. Graph Neural Networks: Graph Generation -- Chapter 12. Graph Neural Networks: Graph Transformation -- Chapter 13. Graph Neural Networks: Graph Matching -- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks -- Chapter 16. Heterogeneous Graph Neural Networks -- Chapter 17. Graph Neural Network: AutoML -- Chapter 18. Graph Neural Networks: Self-supervised Learning -- Chapter 19. Graph Neural Network in Modern Recommender Systems -- Chapter 20. Graph Neural Network in Computer Vision -- Chapter 21. Graph Neural Networks in Natural Language Processing -- Chapter 22. Graph Neural Networks in Program Analysis -- Chapter 23. Graph Neural Networks in Software Mining -- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development" -- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions" -- Chapter 26. Graph Neural Networks in Anomaly Detection -- Chapter 27. Graph Neural Networks in Urban Intelligence.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-981-16-6054-2
ISBN:	9789811660542

Graph neural networks = foundations, frontiers, and applications /
Graph neural networksfoundations, frontiers, and applications /[electronic resource] :edited by Lingfei Wu ... [et al.]. - Singapore :Springer Singapore :2022. - xxxvi, 689 p. :ill., digital ;24 cm.

Chapter 1. Representation Learning -- Chapter 2. Graph Representation Learning -- Chapter 3. Graph Neural Networks -- Chapter 4. Graph Neural Networks for Node Classification -- Chapter 5. The Expressive Power of Graph Neural Networks -- Chapter 6. Graph Neural Networks: Scalability -- Chapter 7. Interpretability in Graph Neural Networks -- Chapter 8. "Graph Neural Networks: Adversarial Robustness" -- Chapter 9. Graph Neural Networks: Graph Classification -- Chapter 10. Graph Neural Networks: Link Prediction -- Chapter 11. Graph Neural Networks: Graph Generation -- Chapter 12. Graph Neural Networks: Graph Transformation -- Chapter 13. Graph Neural Networks: Graph Matching -- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks -- Chapter 16. Heterogeneous Graph Neural Networks -- Chapter 17. Graph Neural Network: AutoML -- Chapter 18. Graph Neural Networks: Self-supervised Learning -- Chapter 19. Graph Neural Network in Modern Recommender Systems -- Chapter 20. Graph Neural Network in Computer Vision -- Chapter 21. Graph Neural Networks in Natural Language Processing -- Chapter 22. Graph Neural Networks in Program Analysis -- Chapter 23. Graph Neural Networks in Software Mining -- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development" -- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions" -- Chapter 26. Graph Neural Networks in Anomaly Detection -- Chapter 27. Graph Neural Networks in Urban Intelligence.

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.

ISBN: 9789811660542

Standard No.: 10.1007/978-981-16-6054-2doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .G73 2022

Dewey Class. No.: 006.32

Graph neural networks = foundations, frontiers, and applications /
LDR:04183nmm a2200325 a 4500 001 2296925
003 DE-He213
005 20220103070550.0
006 m d
007 cr nn 008maaau
008 230324s2022 si s 0 eng d
020 $a 9789811660542 $q (electronic bk.)
020 $a 9789811660535 $q (paper)
024 7 $a 10.1007/978-981-16-6054-2 $2 doi
035 $a 978-981-16-6054-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87 $b .G73 2022
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .G766 2022
245 0 0 $a Graph neural networks $h [electronic resource] : $b foundations, frontiers, and applications / $c edited by Lingfei Wu ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xxxvi, 689 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Representation Learning -- Chapter 2. Graph Representation Learning -- Chapter 3. Graph Neural Networks -- Chapter 4. Graph Neural Networks for Node Classification -- Chapter 5. The Expressive Power of Graph Neural Networks -- Chapter 6. Graph Neural Networks: Scalability -- Chapter 7. Interpretability in Graph Neural Networks -- Chapter 8. "Graph Neural Networks: Adversarial Robustness" -- Chapter 9. Graph Neural Networks: Graph Classification -- Chapter 10. Graph Neural Networks: Link Prediction -- Chapter 11. Graph Neural Networks: Graph Generation -- Chapter 12. Graph Neural Networks: Graph Transformation -- Chapter 13. Graph Neural Networks: Graph Matching -- Chapter 14. "Graph Neural Networks: Graph Structure Learning". Chapter 15. Dynamic Graph Neural Networks -- Chapter 16. Heterogeneous Graph Neural Networks -- Chapter 17. Graph Neural Network: AutoML -- Chapter 18. Graph Neural Networks: Self-supervised Learning -- Chapter 19. Graph Neural Network in Modern Recommender Systems -- Chapter 20. Graph Neural Network in Computer Vision -- Chapter 21. Graph Neural Networks in Natural Language Processing -- Chapter 22. Graph Neural Networks in Program Analysis -- Chapter 23. Graph Neural Networks in Software Mining -- Chapter 24. "GNN-based Biomedical Knowledge Graph Mining in Drug Development" -- Chapter 25. "Graph Neural Networks in Predicting Protein Function and Interactions" -- Chapter 26. Graph Neural Networks in Anomaly Detection -- Chapter 27. Graph Neural Networks in Urban Intelligence.
520 $a 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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Graph theory. $3 523815
650 0 $a Deep learning (Machine learning) $3 3538509
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Theory of Computation. $3 892514
700 1 $a Wu, Lingfei. $3 3591988
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-16-6054-2
950 $a Computer Science (SpringerNature-11645)