Heterogeneous Graph Representation Learning and Applications

by Philip S. Yu, Xiao Wang, Chuan Shi

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"