Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks
As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phyl...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3795 |
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| author | Xiaoyang Liu Nan Ding Giacomo Fiumara Pasquale De Meo Annamaria Ficara |
| author_facet | Xiaoyang Liu Nan Ding Giacomo Fiumara Pasquale De Meo Annamaria Ficara |
| author_sort | Xiaoyang Liu |
| collection | DOAJ |
| description | As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Then, the quadratic form of the error function is minimized. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real networks. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and PisCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. It is proven that the proposed PPPM method is robust, reasonable, and effective. This method can also be applied to the general social networking community discovery field. |
| first_indexed | 2024-03-09T11:12:38Z |
| format | Article |
| id | doaj.art-078d6696f667499db51317b7b305926f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:12:38Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-078d6696f667499db51317b7b305926f2023-12-01T00:38:55ZengMDPI AGApplied Sciences2076-34172022-04-01128379510.3390/app12083795Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal NetworksXiaoyang Liu0Nan Ding1Giacomo Fiumara2Pasquale De Meo3Annamaria Ficara4School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, ChinaMIFT Department, University of Messina, Viale F. S. D’Alcontres 31, 98166 Messina, ItalyDepartment of Ancient and Modern Civilizations, University of Messina, Viale G. Palatucci 13, 98168 Messina, ItalyDepartment of Mathematics and Computer Science, University of Palermo, Via Archirafi 34, 90123 Palermo, ItalyAs most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Then, the quadratic form of the error function is minimized. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real networks. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and PisCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. It is proven that the proposed PPPM method is robust, reasonable, and effective. This method can also be applied to the general social networking community discovery field.https://www.mdpi.com/2076-3417/12/8/3795temporal networkscommunity discoveryphylogenetic evolutionplanted of partition |
| spellingShingle | Xiaoyang Liu Nan Ding Giacomo Fiumara Pasquale De Meo Annamaria Ficara Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks Applied Sciences temporal networks community discovery phylogenetic evolution planted of partition |
| title | Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks |
| title_full | Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks |
| title_fullStr | Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks |
| title_full_unstemmed | Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks |
| title_short | Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks |
| title_sort | dynamic community discovery method based on phylogenetic planted partition in temporal networks |
| topic | temporal networks community discovery phylogenetic evolution planted of partition |
| url | https://www.mdpi.com/2076-3417/12/8/3795 |
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