Description: Heterogeneous Graph Representation Learning and Applications, Hardcover by Shi, Chuan; Wang, Xiao; Yu, Philip S., ISBN 9811661650, ISBN-13 9789811661655, Like New Used, Free shipping in the US 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 (. 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.
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Book Title: Heterogeneous Graph Representation Learning and Applications
Number of Pages: Xx, 318 Pages
Language: English
Publication Name: Heterogeneous Graph Representation Learning and Applications
Publisher: Springer
Subject: Intelligence (Ai) & Semantics, Probability & Statistics / General, General, Databases / Data Mining
Publication Year: 2022
Item Weight: 23.8 Oz
Type: Textbook
Author: Chuan Shi, Philip S. Yu, Xiao Wang
Subject Area: Mathematics, Computers
Item Length: 9.3 in
Item Width: 6.1 in
Series: Artificial Intelligence: Foundations, Theory, and Algorithms Ser.
Format: Hardcover
