Community Detection Based on a Preferential Decision Model
The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the proces...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/1/53 |
| _version_ | 1828775283743784960 |
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| author | Jinfang Sheng Ben Lu Bin Wang Jie Hu Kai Wang Xiaoxia Pan Qiangqiang Dong Dawit Aklilu |
| author_facet | Jinfang Sheng Ben Lu Bin Wang Jie Hu Kai Wang Xiaoxia Pan Qiangqiang Dong Dawit Aklilu |
| author_sort | Jinfang Sheng |
| collection | DOAJ |
| description | The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a <b>C</b>ommunity <b>D</b>etection based on <b>P</b>referential <b>D</b>ecision Model, called <b>CDPD</b>. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks. |
| first_indexed | 2024-12-11T15:37:34Z |
| format | Article |
| id | doaj.art-294b8bb66bb84ccaab412ba801e7a758 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-11T15:37:34Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-294b8bb66bb84ccaab412ba801e7a7582022-12-22T00:59:54ZengMDPI AGInformation2078-24892020-01-011115310.3390/info11010053info11010053Community Detection Based on a Preferential Decision ModelJinfang Sheng0Ben Lu1Bin Wang2Jie Hu3Kai Wang4Xiaoxia Pan5Qiangqiang Dong6Dawit Aklilu7School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaThe research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a <b>C</b>ommunity <b>D</b>etection based on <b>P</b>referential <b>D</b>ecision Model, called <b>CDPD</b>. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.https://www.mdpi.com/2078-2489/11/1/53complex networkscommunity detectionpreferential decisionlabel propagationinformation dynamic |
| spellingShingle | Jinfang Sheng Ben Lu Bin Wang Jie Hu Kai Wang Xiaoxia Pan Qiangqiang Dong Dawit Aklilu Community Detection Based on a Preferential Decision Model Information complex networks community detection preferential decision label propagation information dynamic |
| title | Community Detection Based on a Preferential Decision Model |
| title_full | Community Detection Based on a Preferential Decision Model |
| title_fullStr | Community Detection Based on a Preferential Decision Model |
| title_full_unstemmed | Community Detection Based on a Preferential Decision Model |
| title_short | Community Detection Based on a Preferential Decision Model |
| title_sort | community detection based on a preferential decision model |
| topic | complex networks community detection preferential decision label propagation information dynamic |
| url | https://www.mdpi.com/2078-2489/11/1/53 |
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