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- W4386220481 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." @default.
- W4386220481 created "2023-08-29" @default.
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- W4386220481 date "2023-08-28" @default.
- W4386220481 modified "2023-09-30" @default.
- W4386220481 title "DAG: Dual Attention Graph Representation Learning for Node Classification" @default.
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- W4386220481 doi "https://doi.org/10.3390/math11173691" @default.
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