A modified SIS model with an infective medium on complex networks and its global stability

2011 ◽  
Vol 390 (12) ◽  
pp. 2408-2413 ◽  
Author(s):  
Meng Yang ◽  
Guanrong Chen ◽  
Xinchu Fu
Keyword(s):  
Complex Networks ◽  
Global Stability ◽  
Sis Model

scholarly journals Global stability of a multistrain SIS model with superinfection and patch structure

2020 ◽  
Vol 43 (17) ◽  
pp. 9671-9680
Author(s):  
Attila Dénes ◽  
Yoshiaki Muroya ◽  
Gergely Röst
Keyword(s):  
Global Stability ◽  
Sis Model ◽  
Patch Structure

Global analysis of an SIS model with an infective vector on complex networks

2012 ◽  
Vol 13 (2) ◽  
pp. 543-557 ◽  
Author(s):  
Yi Wang ◽  
Zhen Jin ◽  
Zimo Yang ◽  
Zi-Ke Zhang ◽  
Tao Zhou ◽  
...  
Keyword(s):  
Complex Networks ◽  
Global Analysis ◽  
Sis Model

Global stability and positive recurrence of a stochastic SIS model with Lévy noise perturbation

2019 ◽  
Vol 523 ◽  
pp. 677-690 ◽  
Author(s):  
Tomás Caraballo ◽  
Adel Settati ◽  
Mohamed El Fatini ◽  
Aadil Lahrouz ◽  
Abdelouahid Imlahi
Keyword(s):  
Global Stability ◽  
Lévy Noise ◽  
Sis Model ◽  
Positive Recurrence ◽  
Noise Perturbation ◽  
Levy Noise ◽  
Stochastic Sis Model

scholarly journals MODELING INFORMATION SPREADING ON COMPLEX NETWORKS

2017 ◽  
Vol 33 (1-2) ◽  
Author(s):  
Daniel Trpevski ◽  
Kire Stamenov ◽  
Ljupčo Kocarev
Keyword(s):  
Complex Networks ◽  
Stable State ◽  
Natural Generalization ◽  
Time Step ◽  
Spreading Process ◽  
Sis Model ◽  
Simultaneous Existence ◽  
Information Type ◽  
Types Of Information ◽  
High Degree

A b s t r a c t: In this article we propose a model for the spread of two types of information in networks. The model is a natural generalization of the epidemic susceptible-infective-susceptible(SIS) model. The two information types have different attractiveness, which affects the nodes' decision on which information type to adopt when both arrive at a node in the same time step. At difference with results from other authors, the model shows simultaneous existence of the two information types in the stable state. We give approximations for the average number of nodes informed with each information type at the end of the spreading process when nodes have high degree.

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