Introduction to the Modeling and Analysis of Complex Systems

334 CHAPTER 16. DYNAMICAL NETWORKS I: MODELING(p i )(p i )Infection from otherinfected nodesStaying infected(1 – p r )1 Susceptible2InfectedRecovery (p r )Figure 16.3: Schematic illustration of the state-transition rule of the SIS model.Regarding the updating scheme, I think you probably liked the simplicity of the asynchronousupdating we used for the voter model, so let’s adopt it for this SIS model too.We will first choose a node randomly, and if it is susceptible, then we will randomly chooseone of its neighbors; this is similar to the original “pull” version of the voter model. All youneed is to revise just the updating function as follows:Code 16.6: SIS-model.pyp_i = 0.5 # infection probabilityp_r = 0.5 # recovery probabilitydef update():global ga = rd.choice(g.nodes())if g.node[a][’state’] == 0: # if susceptibleb = rd.choice(g.neighbors(a))if g.node[b][’state’] == 1: # if neighbor b is infectedg.node[a][’state’] = 1 if random() < p_i else 0else: # if infected