Graph Machine Learning
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Beschreibung
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.
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.
Provides a complete, practice oriented roadmap for mastering graph machine learning from fundamentals to advanced GNNs Equips readers with skills through real world cases in recommendation, risk control, and scientific intelligence Offers integrated coverage of trustworthy graph learning, heterogeneous graphs, and emerging graph foundation models
Autor*in
Chuan Shi
Themen in »Graph Machine Learning«
Graph Machine Learning Graph Neural Network Graph Representation Learning Graph Embedding Methods Trustworthy Graph Learning Heterogeneous Graph Learning GNN Graph Learning Deep learning