Research on the Prediction of Operator Users’ Number Portability Based on Community Detection
In 2019, China introduced a policy on Number Portability Management, which has resulted in a rapid increase in the number of lost users among telecom companies. Telecom companies must urgently distinguish those with a tendency toward number portability. However, existing prediction research lacks th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3497 |
| _version_ | 1797613711476654080 |
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| author | Ruixia Chen Binmei Liang |
| author_facet | Ruixia Chen Binmei Liang |
| author_sort | Ruixia Chen |
| collection | DOAJ |
| description | In 2019, China introduced a policy on Number Portability Management, which has resulted in a rapid increase in the number of lost users among telecom companies. Telecom companies must urgently distinguish those with a tendency toward number portability. However, existing prediction research lacks the input of temporal variations in user data and the graph-based analysis of user relationship characteristics, resulting in a poor prediction effect. In this paper, a neural-network-based approach has been applied to address the limitation, whereby user data do not feature temporal variation. Furthermore, innovative approaches have been proposed to construct multilayer community networks through users’ geographic attributes and to analyze community networks with a network embedding method based on the matrix factorization framework. This fills a gap in existing research areas, whereby the geographic attributes of users have not received much attention. Considering the extensive inputs and multiple features of the predicted attributes, in this paper, the strengths and weaknesses of three feature selection methods are compared, as well as the prediction accuracy of each of the five prediction models. Finally, the embedded feature selection method, deep neural network model, and the Light GBM model are shown to provide better results. After introducing the user community network, it was found that the prediction evaluation indicators of both the deep neural network model and the Light GBM model are improved. |
| first_indexed | 2024-03-11T06:59:38Z |
| format | Article |
| id | doaj.art-7e73b38e40564063ae14c9d028ff8db7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:59:38Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7e73b38e40564063ae14c9d028ff8db72023-11-17T09:22:31ZengMDPI AGApplied Sciences2076-34172023-03-01136349710.3390/app13063497Research on the Prediction of Operator Users’ Number Portability Based on Community DetectionRuixia Chen0Binmei Liang1School of Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaSchool of Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaIn 2019, China introduced a policy on Number Portability Management, which has resulted in a rapid increase in the number of lost users among telecom companies. Telecom companies must urgently distinguish those with a tendency toward number portability. However, existing prediction research lacks the input of temporal variations in user data and the graph-based analysis of user relationship characteristics, resulting in a poor prediction effect. In this paper, a neural-network-based approach has been applied to address the limitation, whereby user data do not feature temporal variation. Furthermore, innovative approaches have been proposed to construct multilayer community networks through users’ geographic attributes and to analyze community networks with a network embedding method based on the matrix factorization framework. This fills a gap in existing research areas, whereby the geographic attributes of users have not received much attention. Considering the extensive inputs and multiple features of the predicted attributes, in this paper, the strengths and weaknesses of three feature selection methods are compared, as well as the prediction accuracy of each of the five prediction models. Finally, the embedded feature selection method, deep neural network model, and the Light GBM model are shown to provide better results. After introducing the user community network, it was found that the prediction evaluation indicators of both the deep neural network model and the Light GBM model are improved.https://www.mdpi.com/2076-3417/13/6/3497number portabilityneural networkcommunity detectionfeature selectionclassification model |
| spellingShingle | Ruixia Chen Binmei Liang Research on the Prediction of Operator Users’ Number Portability Based on Community Detection Applied Sciences number portability neural network community detection feature selection classification model |
| title | Research on the Prediction of Operator Users’ Number Portability Based on Community Detection |
| title_full | Research on the Prediction of Operator Users’ Number Portability Based on Community Detection |
| title_fullStr | Research on the Prediction of Operator Users’ Number Portability Based on Community Detection |
| title_full_unstemmed | Research on the Prediction of Operator Users’ Number Portability Based on Community Detection |
| title_short | Research on the Prediction of Operator Users’ Number Portability Based on Community Detection |
| title_sort | research on the prediction of operator users number portability based on community detection |
| topic | number portability neural network community detection feature selection classification model |
| url | https://www.mdpi.com/2076-3417/13/6/3497 |
| work_keys_str_mv | AT ruixiachen researchonthepredictionofoperatorusersnumberportabilitybasedoncommunitydetection AT binmeiliang researchonthepredictionofoperatorusersnumberportabilitybasedoncommunitydetection |