A Community-Based Approach to Identifying Influential Spreaders
Identifying influential spreaders in complex networks has a significant impact on understanding and control of spreading process in networks. In this paper, we introduce a new centrality index to identify influential spreaders in a network based on the community structure of the network. The communi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-04-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/17/4/2228 |
| _version_ | 1798002721967570944 |
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| author | Zhiying Zhao Xiaofan Wang Wei Zhang Zhiliang Zhu |
| author_facet | Zhiying Zhao Xiaofan Wang Wei Zhang Zhiliang Zhu |
| author_sort | Zhiying Zhao |
| collection | DOAJ |
| description | Identifying influential spreaders in complex networks has a significant impact on understanding and control of spreading process in networks. In this paper, we introduce a new centrality index to identify influential spreaders in a network based on the community structure of the network. The community-based centrality (CbC) considers both the number and sizes of communities that are directly linked by a node. We discuss correlations between CbC and other classical centrality indices. Based on simulations of the single source of infection with the Susceptible-Infected-Recovered (SIR) model, we find that CbC can help to identify some critical influential nodes that other indices cannot find. We also investigate the stability of CbC. |
| first_indexed | 2024-04-11T11:56:48Z |
| format | Article |
| id | doaj.art-af3a4a43faa347c297d89e557ef6585d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:56:48Z |
| publishDate | 2015-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-af3a4a43faa347c297d89e557ef6585d2022-12-22T04:25:06ZengMDPI AGEntropy1099-43002015-04-011742228225210.3390/e17042228e17042228A Community-Based Approach to Identifying Influential SpreadersZhiying Zhao0Xiaofan Wang1Wei Zhang2Zhiliang Zhu3College of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, ChinaSoftware College, Northeastern University, Shenyang 110819, ChinaSoftware College, Northeastern University, Shenyang 110819, ChinaIdentifying influential spreaders in complex networks has a significant impact on understanding and control of spreading process in networks. In this paper, we introduce a new centrality index to identify influential spreaders in a network based on the community structure of the network. The community-based centrality (CbC) considers both the number and sizes of communities that are directly linked by a node. We discuss correlations between CbC and other classical centrality indices. Based on simulations of the single source of infection with the Susceptible-Infected-Recovered (SIR) model, we find that CbC can help to identify some critical influential nodes that other indices cannot find. We also investigate the stability of CbC.http://www.mdpi.com/1099-4300/17/4/2228influential spreaderscommunity structurecomplex networks |
| spellingShingle | Zhiying Zhao Xiaofan Wang Wei Zhang Zhiliang Zhu A Community-Based Approach to Identifying Influential Spreaders Entropy influential spreaders community structure complex networks |
| title | A Community-Based Approach to Identifying Influential Spreaders |
| title_full | A Community-Based Approach to Identifying Influential Spreaders |
| title_fullStr | A Community-Based Approach to Identifying Influential Spreaders |
| title_full_unstemmed | A Community-Based Approach to Identifying Influential Spreaders |
| title_short | A Community-Based Approach to Identifying Influential Spreaders |
| title_sort | community based approach to identifying influential spreaders |
| topic | influential spreaders community structure complex networks |
| url | http://www.mdpi.com/1099-4300/17/4/2228 |
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