A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data
Abstract Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data. To delve deeply into the concealed f...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82433-4 |
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| author | Pengyuan Wang Zhengying Wen |
| author_facet | Pengyuan Wang Zhengying Wen |
| author_sort | Pengyuan Wang |
| collection | DOAJ |
| description | Abstract Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data. To delve deeply into the concealed feature attributes within timely spatio-temporal sequence data from social media, this study introduces a Spatio-Temporal Graph Wavelet Neural Network (ST-GWNN). This model captures spatio-temporal correlations across time and space by combining spatial graphs from multiple time intervals. On this basis, we have developed a spatial feature extraction layer using the Graph Wavelet Neural Network (GWNN). This layer learns localized representations of node features to identify spatial dependencies. In GWNN, graph wavelet transformation reduces computational complexity and improves operational efficiency compared to Spectral CNN. Furthermore, the sparse representation of node features is enhanced via localized learning, thereby improving network performance. The effectiveness of the model is verified using four distinct social media datasets. Experimental results underscore the notable advantages of the proposed model in the realm of timely time-series data association mining, showcasing its capacity to better capture spatio-temporal dynamics and uncover the underlying association mining within the data. In comparison to alternative models, the approach outlined in this paper exhibits substantial improvements in terms of accuracy and efficiency, affirming the efficacy and innovation of the model. |
| format | Article |
| id | doaj-art-a8c63f4dee50411d8ea9be90d4b238e6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a8c63f4dee50411d8ea9be90d4b238e62024-12-29T12:31:10ZengNature PortfolioScientific Reports2045-23222024-12-0114111810.1038/s41598-024-82433-4A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media dataPengyuan Wang0Zhengying Wen1Zhengzhou University of Light IndustryHenan University of EngineeringAbstract Social media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data. To delve deeply into the concealed feature attributes within timely spatio-temporal sequence data from social media, this study introduces a Spatio-Temporal Graph Wavelet Neural Network (ST-GWNN). This model captures spatio-temporal correlations across time and space by combining spatial graphs from multiple time intervals. On this basis, we have developed a spatial feature extraction layer using the Graph Wavelet Neural Network (GWNN). This layer learns localized representations of node features to identify spatial dependencies. In GWNN, graph wavelet transformation reduces computational complexity and improves operational efficiency compared to Spectral CNN. Furthermore, the sparse representation of node features is enhanced via localized learning, thereby improving network performance. The effectiveness of the model is verified using four distinct social media datasets. Experimental results underscore the notable advantages of the proposed model in the realm of timely time-series data association mining, showcasing its capacity to better capture spatio-temporal dynamics and uncover the underlying association mining within the data. In comparison to alternative models, the approach outlined in this paper exhibits substantial improvements in terms of accuracy and efficiency, affirming the efficacy and innovation of the model.https://doi.org/10.1038/s41598-024-82433-4Spatio-temporal sequence dataSocial mediaAssociation miningSpatio-temporal graphsGraph wavelet neural networks |
| spellingShingle | Pengyuan Wang Zhengying Wen A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data Scientific Reports Spatio-temporal sequence data Social media Association mining Spatio-temporal graphs Graph wavelet neural networks |
| title | A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data |
| title_full | A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data |
| title_fullStr | A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data |
| title_full_unstemmed | A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data |
| title_short | A spatio-temporal graph wavelet neural network (ST-GWNN) for association mining in timely social media data |
| title_sort | spatio temporal graph wavelet neural network st gwnn for association mining in timely social media data |
| topic | Spatio-temporal sequence data Social media Association mining Spatio-temporal graphs Graph wavelet neural networks |
| url | https://doi.org/10.1038/s41598-024-82433-4 |
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