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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https://doi.org/10.1038/s41598-025-88993-3

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