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Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms) by Chuan Shi; Xiao Wang; Philip S. Yu is a nonfiction available to read on EtoBox.

What is Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms) about?

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, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The bo

Who reads Heterogeneous Graph Representation Learning and Applications (Artificial Intelligence: Foundations, Theory, and Algorithms)?

Author

Chuan Shi; Xiao Wang; Philip S. Yu

Publisher

Springer Singapore : Imprint: Springer

Published

2022

Language

EN

ISBN

9789811661662

Category

nonfiction

Subjects

Management, Science, Mathematics