Heterogeneous Graph Representation Learning and Applications

Language: English

Published by Springer, Springer Jan 2022, 2022

9811661650 / 9789811661655

Series: Book 10 of 15 - Artificial Intelligence: Foundations, Theory, and Algorithms

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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 340 pp. Englisch. …

Seller Inventory # 9789811661655

Title
Heterogeneous Graph Representation Learning and Applications
Author
Chuan Shi
Publisher
Springer, Springer Jan 2022
Publication year
2022
Condition
Neu
Binding
Buch
Language
English
ISBN 10
9811661650
ISBN 13
9789811661655
Item weight
676 grams
Dimensions
241x160x24 mm
Series
Book 10 of 15: Artificial Intelligence: Foundations, Theory, and Algorithms

buchversandmimpf2000

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