PMID- 36359713
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221126
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 24
IP  - 11
DP  - 2022 Nov 8
TI  - Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking.
LID - 10.3390/e24111623 [doi]
LID - 1623
AB  - 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.
FAU - Dai, Caiyan
AU  - Dai C
AD  - College of Artificial Intelligence and Information Technology, Nanjing University 
      of Chinese Medicine, Nanjing 210023, China.
FAU - Chen, Ling
AU  - Chen L
AD  - College of Information Engineering, Yangzhou University, Yangzhou 225012, China.
FAU - Hu, Kongfa
AU  - Hu K
AD  - College of Artificial Intelligence and Information Technology, Nanjing University 
      of Chinese Medicine, Nanjing 210023, China.
AD  - Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in 
      Prevention and Treatment of Tumor, Nanjing 210023, China.
FAU - Ding, Youwei
AU  - Ding Y
AD  - College of Artificial Intelligence and Information Technology, Nanjing University 
      of Chinese Medicine, Nanjing 210023, China.
LA  - eng
PT  - Journal Article
DEP - 20221108
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC9689805
OTO - NOTNLM
OT  - SNIR model
OT  - minimize virus infection
OT  - precise isolation
COIS- The authors declare no conflict of interest.
EDAT- 2022/11/12 06:00
MHDA- 2022/11/12 06:01
PMCR- 2022/11/08
CRDT- 2022/11/11 01:12
PHST- 2022/09/01 00:00 [received]
PHST- 2022/10/29 00:00 [revised]
PHST- 2022/11/07 00:00 [accepted]
PHST- 2022/11/11 01:12 [entrez]
PHST- 2022/11/12 06:00 [pubmed]
PHST- 2022/11/12 06:01 [medline]
PHST- 2022/11/08 00:00 [pmc-release]
AID - e24111623 [pii]
AID - entropy-24-01623 [pii]
AID - 10.3390/e24111623 [doi]
PST - epublish
SO  - Entropy (Basel). 2022 Nov 8;24(11):1623. doi: 10.3390/e24111623.