Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks
Labels provide a quick and effective solution to obtain people interesting content from large-scale social network information. The current interest label extraction method based on the subgraph stream proves the feasibility of the subgraph stream for user label extraction. However, it is extremely...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/2072950 |
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| _version_ | 1832562084838113280 |
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| author | Xiaoling Huang Lei Li Hao Wang Chengxiang Hu Xiaohan Xu Changlin Wu |
| author_facet | Xiaoling Huang Lei Li Hao Wang Chengxiang Hu Xiaohan Xu Changlin Wu |
| author_sort | Xiaoling Huang |
| collection | DOAJ |
| description | Labels provide a quick and effective solution to obtain people interesting content from large-scale social network information. The current interest label extraction method based on the subgraph stream proves the feasibility of the subgraph stream for user label extraction. However, it is extremely time-consuming for constructing subgraphs. As an effective mathematical method to deal with fuzzy and uncertain information, rough set-based representations for subgraph stream construction are capable of capturing the uncertainties of the social network. Therefore, we propose an effective approach called RS_UNITE_SS (namely, rough-set-based user-networked interest topic extraction in the form of subgraph stream), which is suitable for large-scale social network user interest label extraction. Specifically, we first propose the subgraph division algorithm to construct a subgraph stream by incorporating a rough set. Then, the algorithm for user real-time interest label extraction based on upper approximation (RILE) is proposed by using sequentially characteristics of the subgraph. Empirically, we evaluate RS_UNITE_SS over real-world datasets, and experimental results demonstrate that our proposed approach is more computationally efficient than existing methods while achieving higher precision value and MRR value. |
| format | Article |
| id | doaj-art-e386de9b6e144a26a20bbab621d29b49 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e386de9b6e144a26a20bbab621d29b492025-02-03T01:23:36ZengWileyComplexity1099-05262022-01-01202210.1155/2022/2072950Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social NetworksXiaoling Huang0Lei Li1Hao Wang2Chengxiang Hu3Xiaohan Xu4Changlin Wu5School of Computer and Information EngineeringSchool of Computer Science and Information EngineeringSchool of Computer Science and Information EngineeringSchool of Computer and Information EngineeringSchool of Computer Science and Information EngineeringEast China Langyashan Pumped Storage Co. Ltd.Labels provide a quick and effective solution to obtain people interesting content from large-scale social network information. The current interest label extraction method based on the subgraph stream proves the feasibility of the subgraph stream for user label extraction. However, it is extremely time-consuming for constructing subgraphs. As an effective mathematical method to deal with fuzzy and uncertain information, rough set-based representations for subgraph stream construction are capable of capturing the uncertainties of the social network. Therefore, we propose an effective approach called RS_UNITE_SS (namely, rough-set-based user-networked interest topic extraction in the form of subgraph stream), which is suitable for large-scale social network user interest label extraction. Specifically, we first propose the subgraph division algorithm to construct a subgraph stream by incorporating a rough set. Then, the algorithm for user real-time interest label extraction based on upper approximation (RILE) is proposed by using sequentially characteristics of the subgraph. Empirically, we evaluate RS_UNITE_SS over real-world datasets, and experimental results demonstrate that our proposed approach is more computationally efficient than existing methods while achieving higher precision value and MRR value.http://dx.doi.org/10.1155/2022/2072950 |
| spellingShingle | Xiaoling Huang Lei Li Hao Wang Chengxiang Hu Xiaohan Xu Changlin Wu Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks Complexity |
| title | Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks |
| title_full | Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks |
| title_fullStr | Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks |
| title_full_unstemmed | Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks |
| title_short | Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks |
| title_sort | rough set based real time interest label extraction over large scale social networks |
| url | http://dx.doi.org/10.1155/2022/2072950 |
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