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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Heterogeneous Graph Representation Learning and Applications

Heterogeneous Graph Representation Learning and Applications

Shi, Chuan
Wang, Xiao
Yu, Philip S.

Paperback

Series: Artificial Intelligence: Foundations, Theory, and Algorithms

DatabasesGeneral Computers

ISBN10: 9811661685
ISBN13: 9789811661686
Publisher: Springer Nature
Published: Feb 1 2023
Pages: 318
Weight: 1.05
Height: 0.71 Width: 6.14 Depth: 9.21
Language: English
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.

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