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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.
In this research, using historic studies, historic maps, period photographs, and other documentary resources, the author creates a historic Geographic Information System to connect attributes of the heritage of Chinese pioneers in a mining camp with spatial data of the historic site, and uses it as an approach to document, visualize and interpret a complex multidimensional heritage site that lacks historic remains. The result of the research is a rich computerized resource of information of an integrated cultural heritage site documented from historic maps, historic photographs, historic newspaper articles, historic records, and other resources. Based on different research objectives, these documented information aspects could be used separately or together to interpret and visualize the multifaceted spatial history of the Chinese in Virginia City, Montana.
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
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