Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks
Due to the inequality of users’ (nodes’) status and the influence of external forces in the progress of the information propagation in a social network, the infected nodes hold different levels of propagation capacity. For this reason, the infected nodes are classified into two categories: the high...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/5615096 |
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| _version_ | 1832556612469915648 |
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| author | Qian Zhang Xianyong Li Yajun Du Jian Zhu |
| author_facet | Qian Zhang Xianyong Li Yajun Du Jian Zhu |
| author_sort | Qian Zhang |
| collection | DOAJ |
| description | Due to the inequality of users’ (nodes’) status and the influence of external forces in the progress of the information propagation in a social network, the infected nodes hold different levels of propagation capacity. For this reason, the infected nodes are classified into two categories: the high influential infected nodes and the ordinary influential infected nodes which separately account for 20% and 80% by Pareto’s principle. By borrowing the SEIR epidemic model, this paper proposes an SE2IR information propagation model. Meanwhile, the global asymptotical stabilities of the spread-free equilibrium point and local spread equilibrium point are proved for this model. This paper also puts forward a series of information control strategies including perceived values of users, social reinforcement intensity, and information timeliness in the social network. Through simulation experiments without or with control strategies on a real company e-mail network dataset, this paper verifies the stability and correctness of the model and the feasibility and effectiveness of the control strategies in the information propagation process, presenting that the model is closer to the real process of the information propagation in the social network. |
| format | Article |
| id | doaj-art-665fa98b9e174868b9d99ceb4145ceba |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-665fa98b9e174868b9d99ceb4145ceba2025-02-03T05:44:48ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/56150965615096Dynamical Analysis of an SE2IR Information Propagation Model in Social NetworksQian Zhang0Xianyong Li1Yajun Du2Jian Zhu3School of Computer and Software Engineering, Xihua University, Chengdu 610039, ChinaSchool of Computer and Software Engineering, Xihua University, Chengdu 610039, ChinaSchool of Computer and Software Engineering, Xihua University, Chengdu 610039, ChinaDepartment of Mathematics and Physics, Xinjiang Institute of Engineering, Urumqi 830023, ChinaDue to the inequality of users’ (nodes’) status and the influence of external forces in the progress of the information propagation in a social network, the infected nodes hold different levels of propagation capacity. For this reason, the infected nodes are classified into two categories: the high influential infected nodes and the ordinary influential infected nodes which separately account for 20% and 80% by Pareto’s principle. By borrowing the SEIR epidemic model, this paper proposes an SE2IR information propagation model. Meanwhile, the global asymptotical stabilities of the spread-free equilibrium point and local spread equilibrium point are proved for this model. This paper also puts forward a series of information control strategies including perceived values of users, social reinforcement intensity, and information timeliness in the social network. Through simulation experiments without or with control strategies on a real company e-mail network dataset, this paper verifies the stability and correctness of the model and the feasibility and effectiveness of the control strategies in the information propagation process, presenting that the model is closer to the real process of the information propagation in the social network.http://dx.doi.org/10.1155/2021/5615096 |
| spellingShingle | Qian Zhang Xianyong Li Yajun Du Jian Zhu Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks Discrete Dynamics in Nature and Society |
| title | Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks |
| title_full | Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks |
| title_fullStr | Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks |
| title_full_unstemmed | Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks |
| title_short | Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks |
| title_sort | dynamical analysis of an se2ir information propagation model in social networks |
| url | http://dx.doi.org/10.1155/2021/5615096 |
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