Advances in Graph Neural Networks
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Beschreibung
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
In addition, this book:
- Provides a comprehensive introduction to the foundations and frontiers of graph neural networks and also summarizes the basic concepts and terminology in graph modeling
- Utilizes graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology
- Presents heterogeneous graph representation learning alongside homogeneous graph representation and Euclidean graph neural networks methods
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
Introduces the foundations and frontiers of graph neural networks Utilizes graph data to describe pairwise relations for real-world data from many different domains Summarizes the basic concepts and terminology in graph modeling
Autor*in
Chuan Shi
Themen in »Advances in Graph Neural Networks«
Graph Neural Networks Dynamic Graphs Graph Structure Learning Machine Learning Data Mining Knowledge Discovery Distilling Graph Neural Networks Heterogeneous Graphs Homogeneous Graphs Intelligent Telecommunications