DAG: Dual Attention Graph Representation Learning for Node Classification

Siyi Lin, Jie Hong (), Bo Lang and Lin Huang ()
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Siyi Lin: School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
Jie Hong: Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, China
Bo Lang: Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
Lin Huang: Department of Engineering and Engineering Technology, Metropolitan State University of Denver, Denver, CO 80217-3362, USA

Mathematics, 2023, vol. 11, issue 17, 1-16

Abstract: Transformer-based graph neural networks have accomplished notable achievements by utilizing the self-attention mechanism for message passing in various domains. However, traditional methods overlook the diverse significance of intra-node representations, focusing solely on internode interactions. To overcome this limitation, we propose a DAG (Dual Attention Graph), a novel approach that integrates both intra-node and internode dynamics for node classification tasks. By considering the information exchange process between nodes from dual branches, DAG provides a holistic understanding of information propagation within graphs, enhancing the interpretability of graph-based machine learning applications. The experimental evaluations demonstrate that DAG excels in node classification tasks, outperforming current benchmark models across ten datasets.

Keywords: graph neural network; message-passing mechanism; node classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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