Predicting co-word links via heterogeneous graph convolutional networks
Abstract Co-word analysis, which explores the co-occurrence of key terminology within a specific field, is a valuable tool for identifying research themes and their networks. Leveraging the booming machine learning models, link prediction in co-word networks makes it possible to discover potential i...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05853-w |
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| author | Yangmin Li Xin Zhang Xin Bai Sen Bai Zhengang Jiang |
| author_facet | Yangmin Li Xin Zhang Xin Bai Sen Bai Zhengang Jiang |
| author_sort | Yangmin Li |
| collection | DOAJ |
| description | Abstract Co-word analysis, which explores the co-occurrence of key terminology within a specific field, is a valuable tool for identifying research themes and their networks. Leveraging the booming machine learning models, link prediction in co-word networks makes it possible to discover potential interactions between research themes and reveal emerging trends. Nevertheless, few existing methods have explored end-to-end deep models, impeded by the limitations of text graph models in learning both word co-occurrence and word-document relations implicit in co-word networks simultaneously. In this work, we propose to use a heterogeneous graph convolutional network (GCN) modeling to jointly learn word embeddings and document embeddings directly from co-word networks, incorporating document-specific information. The learning model is supervised by the binary labels for the existence of co-word links. Extensive experiments have been conducted on the Web of Science dataset from Information Science and Library Science. Experimental results show that the AUC value of our GCN-based approach is $$93.46\%$$ , whereas the AUC value of the best traditional machine learning method is $$89.15\%$$ . |
| format | Article |
| id | doaj-art-91f3d239bc594cd6ab09f316164e2e6c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-91f3d239bc594cd6ab09f316164e2e6c2025-08-20T03:45:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-05853-wPredicting co-word links via heterogeneous graph convolutional networksYangmin Li0Xin Zhang1Xin Bai2Sen Bai3Zhengang Jiang4School of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologyResearch Department, Huawei Technologies Co. LtdSchool of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologyAbstract Co-word analysis, which explores the co-occurrence of key terminology within a specific field, is a valuable tool for identifying research themes and their networks. Leveraging the booming machine learning models, link prediction in co-word networks makes it possible to discover potential interactions between research themes and reveal emerging trends. Nevertheless, few existing methods have explored end-to-end deep models, impeded by the limitations of text graph models in learning both word co-occurrence and word-document relations implicit in co-word networks simultaneously. In this work, we propose to use a heterogeneous graph convolutional network (GCN) modeling to jointly learn word embeddings and document embeddings directly from co-word networks, incorporating document-specific information. The learning model is supervised by the binary labels for the existence of co-word links. Extensive experiments have been conducted on the Web of Science dataset from Information Science and Library Science. Experimental results show that the AUC value of our GCN-based approach is $$93.46\%$$ , whereas the AUC value of the best traditional machine learning method is $$89.15\%$$ .https://doi.org/10.1038/s41598-025-05853-wCo-word networksGraph convolutional networksLink prediction |
| spellingShingle | Yangmin Li Xin Zhang Xin Bai Sen Bai Zhengang Jiang Predicting co-word links via heterogeneous graph convolutional networks Scientific Reports Co-word networks Graph convolutional networks Link prediction |
| title | Predicting co-word links via heterogeneous graph convolutional networks |
| title_full | Predicting co-word links via heterogeneous graph convolutional networks |
| title_fullStr | Predicting co-word links via heterogeneous graph convolutional networks |
| title_full_unstemmed | Predicting co-word links via heterogeneous graph convolutional networks |
| title_short | Predicting co-word links via heterogeneous graph convolutional networks |
| title_sort | predicting co word links via heterogeneous graph convolutional networks |
| topic | Co-word networks Graph convolutional networks Link prediction |
| url | https://doi.org/10.1038/s41598-025-05853-w |
| work_keys_str_mv | AT yangminli predictingcowordlinksviaheterogeneousgraphconvolutionalnetworks AT xinzhang predictingcowordlinksviaheterogeneousgraphconvolutionalnetworks AT xinbai predictingcowordlinksviaheterogeneousgraphconvolutionalnetworks AT senbai predictingcowordlinksviaheterogeneousgraphconvolutionalnetworks AT zhengangjiang predictingcowordlinksviaheterogeneousgraphconvolutionalnetworks |