A Novel Link Prediction Framework Based on Gravitational Field
Abstract Currently, most researchers only utilize the network information or node characteristics to calculate the connection probability between unconnected node pairs. Therefore, we attempt to project the problem of connection probability between unconnected pairs into the physical space calculati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-01-01
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| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41019-022-00201-8 |
| _version_ | 1797863964574482432 |
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| author | Yanlin Yang Zhonglin Ye Haixing Zhao Lei Meng |
| author_facet | Yanlin Yang Zhonglin Ye Haixing Zhao Lei Meng |
| author_sort | Yanlin Yang |
| collection | DOAJ |
| description | Abstract Currently, most researchers only utilize the network information or node characteristics to calculate the connection probability between unconnected node pairs. Therefore, we attempt to project the problem of connection probability between unconnected pairs into the physical space calculating it. Firstly, the definition of gravitation is introduced in this paper, and the concept of gravitation is used to measure the strength of the relationship between nodes in complex networks. It is generally known that the gravitational value is related to the mass of objects and the distance between objects. In complex networks, the interrelationship between nodes is related to the characteristics, degree, betweenness, and importance of the nodes themselves, as well as the distance between nodes, which is very similar to the gravitational relationship between objects. Therefore, the importance of nodes is used to measure the mass property in the universal gravitational equation and the similarity between nodes is used to measure the distance property in the universal gravitational equation, and then a complex network model is constructed from physical space. Secondly, the direct and indirect gravitational values between nodes are considered, and a novel link prediction framework based on the gravitational field, abbreviated as LPFGF, is proposed, as well as the node similarity framework equation. Then, the framework is extended to various link prediction algorithms such as Common Neighbors (CN), Adamic-Adar (AA), Preferential Attachment (PA), and Local Random Walk (LRW), resulting in the proposed link prediction algorithms LPFGF-CN, LPFGF-AA, LPFGF-PA, LPFGF-LRW, and so on. Finally, four real datasets are used to compare prediction performance, and the results demonstrate that the proposed algorithmic framework can successfully improve the prediction performance of other link prediction algorithms, with a maximum improvement of 15%. |
| first_indexed | 2024-04-09T22:44:04Z |
| format | Article |
| id | doaj.art-b0ac30e43c3242eb90900f69e04dbe85 |
| institution | Directory Open Access Journal |
| issn | 2364-1185 2364-1541 |
| language | English |
| last_indexed | 2024-04-09T22:44:04Z |
| publishDate | 2023-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj.art-b0ac30e43c3242eb90900f69e04dbe852023-03-22T11:58:35ZengSpringerOpenData Science and Engineering2364-11852364-15412023-01-0181476010.1007/s41019-022-00201-8A Novel Link Prediction Framework Based on Gravitational FieldYanlin Yang0Zhonglin Ye1Haixing Zhao2Lei Meng3College of Computer, Qinghai Normal UniversityCollege of Computer, Qinghai Normal UniversityCollege of Computer, Qinghai Normal UniversityCollege of Computer, Qinghai Normal UniversityAbstract Currently, most researchers only utilize the network information or node characteristics to calculate the connection probability between unconnected node pairs. Therefore, we attempt to project the problem of connection probability between unconnected pairs into the physical space calculating it. Firstly, the definition of gravitation is introduced in this paper, and the concept of gravitation is used to measure the strength of the relationship between nodes in complex networks. It is generally known that the gravitational value is related to the mass of objects and the distance between objects. In complex networks, the interrelationship between nodes is related to the characteristics, degree, betweenness, and importance of the nodes themselves, as well as the distance between nodes, which is very similar to the gravitational relationship between objects. Therefore, the importance of nodes is used to measure the mass property in the universal gravitational equation and the similarity between nodes is used to measure the distance property in the universal gravitational equation, and then a complex network model is constructed from physical space. Secondly, the direct and indirect gravitational values between nodes are considered, and a novel link prediction framework based on the gravitational field, abbreviated as LPFGF, is proposed, as well as the node similarity framework equation. Then, the framework is extended to various link prediction algorithms such as Common Neighbors (CN), Adamic-Adar (AA), Preferential Attachment (PA), and Local Random Walk (LRW), resulting in the proposed link prediction algorithms LPFGF-CN, LPFGF-AA, LPFGF-PA, LPFGF-LRW, and so on. Finally, four real datasets are used to compare prediction performance, and the results demonstrate that the proposed algorithmic framework can successfully improve the prediction performance of other link prediction algorithms, with a maximum improvement of 15%.https://doi.org/10.1007/s41019-022-00201-8Link predictionGravitational fieldNode importanceSimilarity matrix |
| spellingShingle | Yanlin Yang Zhonglin Ye Haixing Zhao Lei Meng A Novel Link Prediction Framework Based on Gravitational Field Data Science and Engineering Link prediction Gravitational field Node importance Similarity matrix |
| title | A Novel Link Prediction Framework Based on Gravitational Field |
| title_full | A Novel Link Prediction Framework Based on Gravitational Field |
| title_fullStr | A Novel Link Prediction Framework Based on Gravitational Field |
| title_full_unstemmed | A Novel Link Prediction Framework Based on Gravitational Field |
| title_short | A Novel Link Prediction Framework Based on Gravitational Field |
| title_sort | novel link prediction framework based on gravitational field |
| topic | Link prediction Gravitational field Node importance Similarity matrix |
| url | https://doi.org/10.1007/s41019-022-00201-8 |
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