Multi-source detection based on neighborhood entropy in social networks
Abstract The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-09229-2 |
| _version_ | 1811260378921828352 |
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| author | YanXia Liu WeiMin Li Chao Yang JianJia Wang |
| author_facet | YanXia Liu WeiMin Li Chao Yang JianJia Wang |
| author_sort | YanXia Liu |
| collection | DOAJ |
| description | Abstract The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes, resulting in a poor source localization effect. In addition, most existing methods ignore the existence of multiple source nodes in the infected cluster and hard to identify the source nodes comprehensively. To solve these problems, we propose a new method about the multiple sources location with the neighborhood entropy. The method first defines the two kinds of entropy, i.e. infection adjacency entropy and infection intensity entropy, depending on whether neighbor nodes are infected or not. Then, the possibility of a node is evaluated by the neighborhood entropy. To locate the source nodes comprehensively, we propose a source location algorithm with the infected clusters. Other unrecognized source nodes in the infection cluster are identified by the cohesion of nodes, which can deal with the situation in the multiple source nodes in an infected cluster. We conduct experiments on various network topologies. Experimental results show that the two proposed algorithms outperform the existing methods. |
| first_indexed | 2024-04-12T18:45:30Z |
| format | Article |
| id | doaj.art-aac68c0ff0a7465e967098b98d359f40 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T18:45:30Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-aac68c0ff0a7465e967098b98d359f402022-12-22T03:20:37ZengNature PortfolioScientific Reports2045-23222022-03-0112111210.1038/s41598-022-09229-2Multi-source detection based on neighborhood entropy in social networksYanXia Liu0WeiMin Li1Chao Yang2JianJia Wang3School of Computer Engineering and Science, Shanghai UniversitySchool of Computer Engineering and Science, Shanghai UniversityShanghai Lixin University of Accounting and FinanceSchool of Computer Engineering and Science, Shanghai UniversityAbstract The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes, resulting in a poor source localization effect. In addition, most existing methods ignore the existence of multiple source nodes in the infected cluster and hard to identify the source nodes comprehensively. To solve these problems, we propose a new method about the multiple sources location with the neighborhood entropy. The method first defines the two kinds of entropy, i.e. infection adjacency entropy and infection intensity entropy, depending on whether neighbor nodes are infected or not. Then, the possibility of a node is evaluated by the neighborhood entropy. To locate the source nodes comprehensively, we propose a source location algorithm with the infected clusters. Other unrecognized source nodes in the infection cluster are identified by the cohesion of nodes, which can deal with the situation in the multiple source nodes in an infected cluster. We conduct experiments on various network topologies. Experimental results show that the two proposed algorithms outperform the existing methods.https://doi.org/10.1038/s41598-022-09229-2 |
| spellingShingle | YanXia Liu WeiMin Li Chao Yang JianJia Wang Multi-source detection based on neighborhood entropy in social networks Scientific Reports |
| title | Multi-source detection based on neighborhood entropy in social networks |
| title_full | Multi-source detection based on neighborhood entropy in social networks |
| title_fullStr | Multi-source detection based on neighborhood entropy in social networks |
| title_full_unstemmed | Multi-source detection based on neighborhood entropy in social networks |
| title_short | Multi-source detection based on neighborhood entropy in social networks |
| title_sort | multi source detection based on neighborhood entropy in social networks |
| url | https://doi.org/10.1038/s41598-022-09229-2 |
| work_keys_str_mv | AT yanxialiu multisourcedetectionbasedonneighborhoodentropyinsocialnetworks AT weiminli multisourcedetectionbasedonneighborhoodentropyinsocialnetworks AT chaoyang multisourcedetectionbasedonneighborhoodentropyinsocialnetworks AT jianjiawang multisourcedetectionbasedonneighborhoodentropyinsocialnetworks |