Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction

Zhan Guo, Mingxin Lu () and Jin Han
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Zhan Guo: School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Mingxin Lu: Department of Information Management, Nanjing University, Nanjing 210044, China
Jin Han: School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

Mathematics, 2025, vol. 13, issue 5, 1-15

Abstract: Comprehensively extracting spatio-temporal features is essential to research topic trend prediction. This necessity arises from the fact that research topics exhibit both temporal trend features and spatial correlation features. This study proposes a Temporal Graph Attention Network (T-GAT) to extract the spatio-temporal features of research topics and predict their trends. In this model, a temporal convolutional layer is employed to extract temporal trend features from multivariate topic time series. Additionally, a multi-head graph attention layer is introduced to capture spatial correlation features among research topics. This layer learns attention scores from the data by using scaled dot product operations and updates edge weights between topics accordingly, thereby mitigating the issue of over-smoothing. Furthermore, we introduce WFtopic-econ and WFtopic-polit, two domain-specific datasets for Chinese research topics constructed from the Wanfang Academic Database. 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 R 2 improved by 4.8% to 7.9% across varying observation time steps on the WFtopic-econ dataset. Moreover, on the WFtopic-polit dataset, RMSE and MAE were reduced by 4.0% to 5.3% and 10.0% to 10.7%, respectively, and R 2 improved by 7.6% to 14.4%. These results validate the effectiveness of integrating graph attention with temporal convolution to model the spatio-temporal evolution of research topics, providing a robust tool for scholarly trend analysis and decision making.

Keywords: topic trend prediction; feature extraction; graph attention; temporal convolution; multivariate time-series forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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