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    Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction by Zhan Guo, Mingxin Lu, Jin Han

    Published 2025-02-01
    “…Extensive experiments demonstrate that T-GAT outperforms baseline models in prediction accuracy, with RMSE and MAE being reduced by 4.8% to 7.1% and 14.5% to 18.4%, respectively, while <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> improved by 4.8% to 7.9% across varying observation time steps on the WFtopic-econ dataset. …”
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