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
Автор: Chuan Shi, Xiao Wang, Philip S. Yu
Издательство: Springer
Серия: Artificial Intelligence: Foundations, Theory, and Algorithms
Год: 2022
Страниц: 329
Язык: английский
Формат: pdf (true), epub
Размер: 33.7 MB
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. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of Computer Science, Data Mining and Machine Learning.