GTAT: empowering graph neural networks with cross attention

GTAT: empowering graph neural networks with cross attention

Abstract Graph Neural Networks (GNNs) serve as a powerful framework for representation learning on graph-structured data, capturing the information of nodes by recursively aggregating and transforming the neighboring nodes’ representations. Topology in graph plays an important role in learning graph...

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Bibliographic Details
Main Authors:	Jiahao Shen, Qura Tul Ain, Yaohua Liu, Banqing Liang, Xiaoli Qiang, Zheng Kou
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-02-01
Series:	Scientific Reports
Subjects:	Graph learning Graph neural networks Network topology Cross attention mechanism
Online Access:	https://doi.org/10.1038/s41598-025-88993-3
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