Heterogeneous Graph Representation Learning and Applications

Language: English

Published by Springer Nature Singapore 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 - it takes 3-4 days longer - 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, feware 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 book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. 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. 340 pp. Englisch.…

Seller Inventory # 9789811661655

Title
Heterogeneous Graph Representation Learning and Applications
Author
Chuan Shi
Publisher
Springer Nature Singapore 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

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

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