Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking
This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomati...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/11/1623 |
| _version_ | 1797468296463777792 |
|---|---|
| author | Caiyan Dai Ling Chen Kongfa Hu Youwei Ding |
| author_facet | Caiyan Dai Ling Chen Kongfa Hu Youwei Ding |
| author_sort | Caiyan Dai |
| collection | DOAJ |
| description | This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections. |
| first_indexed | 2024-03-09T19:05:24Z |
| format | Article |
| id | doaj.art-5d45fe8595ee46eeb4d53f84bbea3743 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T19:05:24Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5d45fe8595ee46eeb4d53f84bbea37432023-11-24T04:37:15ZengMDPI AGEntropy1099-43002022-11-012411162310.3390/e24111623Minimizing the Spread of Negative Influence in SNIR Model by Contact BlockingCaiyan Dai0Ling Chen1Kongfa Hu2Youwei Ding3College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou 225012, ChinaCollege of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, ChinaCollege of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, ChinaThis paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections.https://www.mdpi.com/1099-4300/24/11/1623precise isolationminimize virus infectionSNIR model |
| spellingShingle | Caiyan Dai Ling Chen Kongfa Hu Youwei Ding Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking Entropy precise isolation minimize virus infection SNIR model |
| title | Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking |
| title_full | Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking |
| title_fullStr | Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking |
| title_full_unstemmed | Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking |
| title_short | Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking |
| title_sort | minimizing the spread of negative influence in snir model by contact blocking |
| topic | precise isolation minimize virus infection SNIR model |
| url | https://www.mdpi.com/1099-4300/24/11/1623 |
| work_keys_str_mv | AT caiyandai minimizingthespreadofnegativeinfluenceinsnirmodelbycontactblocking AT lingchen minimizingthespreadofnegativeinfluenceinsnirmodelbycontactblocking AT kongfahu minimizingthespreadofnegativeinfluenceinsnirmodelbycontactblocking AT youweiding minimizingthespreadofnegativeinfluenceinsnirmodelbycontactblocking |