Heterogeneous Graph Representation Learning and Applications
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Book Overview
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...
Format:Hardcover
Language:English
ISBN:9811661650
ISBN13:9789811661655
Release Date:January 2022
Publisher:Springer
Length:318 Pages
Weight:1.44 lbs.
Dimensions:0.8" x 6.1" x 9.2"
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