Introduction to Graph Neural Networks
-
- Taschenbuch
- eBook ausgewählt
-
Form:Einzelkauf Download
-
Sprache:Englisch
64,19 €
inkl. MwStBeschreibung
Details
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
31.05.2022
Verlag
SpringerSeitenzahl
109 (Printausgabe)
Dateigröße
24314 KB
Sprache
Englisch
EAN
9783031015878
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.
This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.Weitere Bände von Synthesis Lectures on Artificial Intelligence and Machine Learning
-
Lifelong Machine Learning, Second Edition von Zhiyuan Chen
Zhiyuan Chen
Lifelong Machine Learning, Second EditioneBook
69,54 €
-
Transfer Learning for Multiagent Reinforcement Learning Systems von Felipe Leno Da Silva
Felipe Leno Da Silva
Transfer Learning for Multiagent Reinforcement Learning SystemseBook
64,19 €
-
Human Computation von Edith Law
Edith Law
Human ComputationeBook
28,88 €
-
Reasoning with Probabilistic and Deterministic Graphical Models von Rina Dechter
Rina Dechter
Reasoning with Probabilistic and Deterministic Graphical ModelseBook
58,84 €
-
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence von Nikos Vlassis
Nikos Vlassis
A Concise Introduction to Multiagent Systems and Distributed Artificial IntelligenceeBook
35,30 €
-
Robot Learning from Human Teachers von Sonia Chernova
Sonia Chernova
Robot Learning from Human TeacherseBook
35,30 €
-
Explainable Human-AI Interaction von Sarath Sreedharan
Sarath Sreedharan
Explainable Human-AI InteractioneBook
58,84 €
-
Action Programming Languages von Michael Thielscher
Michael Thielscher
Action Programming LanguageseBook
28,88 €
-
Data Integration von Michael Genesereth
Michael Genesereth
Data IntegrationeBook
35,30 €
-
Algorithms for Reinforcement Learning von Csaba Szepesvári
Csaba Szepesvári
Algorithms for Reinforcement LearningeBook
32,09 €
-
Introduction to Graph Neural Networks von Zhiyuan Liu
Zhiyuan Liu
Introduction to Graph Neural NetworkseBook
64,19 €
-
Metric Learning von Aurélien Bellet
Aurélien Bellet
Metric LearningeBook
64,19 €
Unsere Kundinnen und Kunden meinen
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kund*innen durch Ihre Meinung
Kurze Frage zu unserer Seite
Vielen Dank für dein Feedback
Wir nutzen dein Feedback, um unsere Produktseiten zu verbessern. Bitte habe Verständnis, dass wir dir keine Rückmeldung geben können. Falls du Kontakt mit uns aufnehmen möchtest, kannst du dich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice