A Classification Method for Academic Resources Based on a Graph Attention Network
Classification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/13/3/64 |
| _version_ | 1827602959752495104 |
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| author | Jie Yu Yaliu Li Chenle Pan Junwei Wang |
| author_facet | Jie Yu Yaliu Li Chenle Pan Junwei Wang |
| author_sort | Jie Yu |
| collection | DOAJ |
| description | Classification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between academic resources, leading to unsatisfactory classification results. In this paper, we propose an Association Content Graph Attention Network (ACGAT), which is based on the association features and content attributes of academic resources. The semantic relevance and academic relevance are introduced into the model. The ACGAT makes full use of the association commonality and the influence information of resources and introduces an attention mechanism to improve the accuracy of academic resource classification. We conducted experiments on a self-built scholar network and two public citation networks. Experimental results show that the ACGAT has better effectiveness than existing classification methods. |
| first_indexed | 2024-03-09T05:29:56Z |
| format | Article |
| id | doaj.art-11ff75a074f84f4db56face914e003a3 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T05:29:56Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-11ff75a074f84f4db56face914e003a32023-12-03T12:33:02ZengMDPI AGFuture Internet1999-59032021-03-011336410.3390/fi13030064A Classification Method for Academic Resources Based on a Graph Attention NetworkJie Yu0Yaliu Li1Chenle Pan2Junwei Wang3School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaClassification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between academic resources, leading to unsatisfactory classification results. In this paper, we propose an Association Content Graph Attention Network (ACGAT), which is based on the association features and content attributes of academic resources. The semantic relevance and academic relevance are introduced into the model. The ACGAT makes full use of the association commonality and the influence information of resources and introduces an attention mechanism to improve the accuracy of academic resource classification. We conducted experiments on a self-built scholar network and two public citation networks. Experimental results show that the ACGAT has better effectiveness than existing classification methods.https://www.mdpi.com/1999-5903/13/3/64academic resourceattentionassociation featurescontent attributesclassification |
| spellingShingle | Jie Yu Yaliu Li Chenle Pan Junwei Wang A Classification Method for Academic Resources Based on a Graph Attention Network Future Internet academic resource attention association features content attributes classification |
| title | A Classification Method for Academic Resources Based on a Graph Attention Network |
| title_full | A Classification Method for Academic Resources Based on a Graph Attention Network |
| title_fullStr | A Classification Method for Academic Resources Based on a Graph Attention Network |
| title_full_unstemmed | A Classification Method for Academic Resources Based on a Graph Attention Network |
| title_short | A Classification Method for Academic Resources Based on a Graph Attention Network |
| title_sort | classification method for academic resources based on a graph attention network |
| topic | academic resource attention association features content attributes classification |
| url | https://www.mdpi.com/1999-5903/13/3/64 |
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