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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...
Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however,...
This book covers the fundamental principles behind the design of ultra-low power radios and how they can form networks to facilitate a variety of applications within healthcare and environmental monitoring, since they may operate for years off a small battery or even...
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