HGNN-GAMS: Heterogeneous Graph Neural Networks for Graph Attribute Mining and Semantic Fusion

HGNN-GAMS: Heterogeneous Graph Neural Networks for Graph Attribute Mining and Semantic Fusion

Heterogeneous Graph Neural Networks (HGNNs) have attracted significant research attention in recent years due to their ability to capture complex interactions among various node types in heterogeneous graphs (HGs). However, existing methods face critical challenges, including the loss of graph attri...

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Bibliographic Details
Main Authors: Yufei Zhao, Hua Liu, Hua Duan
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Graph embedding
neural networks
heterogeneous graphs
graph representation learning
Online Access:https://ieeexplore.ieee.org/document/10804165/

Internet

https://ieeexplore.ieee.org/document/10804165/

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