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001443457 050_4 $$aQA76.87$$b.G73 2022
001443457 08204 $$a006.3/2$$223
001443457 24500 $$aGraph neural networks :$$bfoundations, frontiers, and applications /$$cLingfei Wu, Peng Cui, Jian Pei, Liang Zhao, editors.
001443457 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001443457 264_4 $$c©2022
001443457 300__ $$a1 online resource
001443457 336__ $$atext$$btxt$$2rdacontent
001443457 337__ $$acomputer$$bc$$2rdamedia
001443457 338__ $$aonline resource$$bcr$$2rdacarrier
001443457 347__ $$atext file$$bPDF$$2rda
001443457 504__ $$aIncludes bibliographical references.
001443457 5050_ $$aChapter 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. .
001443457 506__ $$aAccess limited to authorized users.
001443457 520__ $$aDeep 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.
001443457 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 26, 2022).
001443457 650_0 $$aNeural networks (Computer science)
001443457 650_0 $$aGraph theory.
001443457 650_0 $$aDeep learning (Machine learning)
001443457 650_6 $$aRéseaux neuronaux (Informatique)
001443457 655_0 $$aElectronic books.
001443457 7001_ $$aWu, Lingfei,$$eeditor.
001443457 7001_ $$aCui, Peng,$$eeditor.
001443457 7001_ $$aPei, Jian$$c(Computer scientist),$$eeditor.
001443457 7001_ $$aZhao, Liang,$$cDr.,$$eeditor.
001443457 77608 $$iPrint version:$$z9811660530$$z9789811660535$$w(OCoLC)1263865253
001443457 852__ $$bebk
001443457 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-6054-2$$zOnline Access$$91397441.1
001443457 909CO $$ooai:library.usi.edu:1443457$$pGLOBAL_SET
001443457 980__ $$aBIB
001443457 980__ $$aEBOOK
001443457 982__ $$aEbook
001443457 983__ $$aOnline
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