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Graph Machine Learning
Chuan Shi, Cheng Yang, Xiao Wang, Zhiqiang Zhang (Gebundene Ausgabe, Englisch)
Optischer Zustand
Beschreibung
Graph machine learning is rapidly reshaping how we model complex relationships—and this book offers one of the most comprehensive, practice oriented guides to mastering it from the ground up. Designed for readers who want both conceptual clarity and hands on capability, it distills the essential foundations of graph representation, graph embedding, and graph neural networks into a clear,… structured learning path. Across fifteen chapters, the book moves from fundamental graph concepts to advanced Graph Neural Network (GNN) architectures, trustworthy graph learning, spectral methods, heterogeneous graphs, and emerging graph foundation models. It not only explains the design logic behind modern graph learning algorithms but also reveals why certain models succeed—or fail—across real world tasks. Readers will explore practical scenarios in social recommendation, financial risk control, and scientific intelligence, gaining the ability to translate theory into effective solutions. The book also highlights frontier directions such as dynamic graphs, hypergraphs, large scale graph learning, and multimodal integration. This book is ideal for university students, engineers, and technical professionals seeking a rigorous yet accessible entry point into graph machine learning. With its combination of conceptual frameworks, platform tools, code implementations, and application case studies, it equips readers to build, optimize, and deploy graph models with confidence—requiring only basic machine learning literacy as a starting point. Dieses Produkt haben wir gerade leider nicht auf Lager.
Handgeprüfte Gebrauchtware
Bis zu 50 % günstiger als neu
Der Umwelt zuliebe
Technische Daten
Erscheinungsdatum
06.01.2027
Sprache
Englisch
EAN
9789819258895
Herausgeber
Springer Singapore
Sonderedition
Nein
Autor
Chuan Shi, Cheng Yang, Xiao Wang, Zhiqiang Zhang
Einbandart
Gebundene Ausgabe
Schlagwörter
Graph Machine Learning, Graph Neural Network, Graph Representation Learning, Graph Embedding Methods, Trustworthy Graph Learning, Heterogeneous Graph Learning, GNN, Graph Learning, Deep learning
Thema-Inhalt
UYQM - Maschinelles Lernen
UYQE - Wissensbasierte Systeme, Expertensysteme
UYQ - Künstliche Intelligenz
Höhe
235 mm
Breite
15.5 cm
Transparenz & Sicherheit
Hersteller: Springer Nature Customer Service Center GmbH, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com
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