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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. 

Advances in Graph Neural Networks

Introduction.- Fundamental Graph Neural Networks.- Homogeneous Graph Neural Networks.- Heterogeneous Graph Neural Networks.- Dynamic Graph Neural Networks.- Hyperbolic Graph Neural Networks.- Distilling Graph Neural Networks.-  Platforms and Practice of Graph Neural Networks.- Future Direction and Conclusion.- References.