Introduction to the Modeling and Analysis of Complex Systems

362 CHAPTER 16. DYNAMICAL NETWORKS I: MODELINGThis new assumption is inspired by a pioneering adaptive network model of epidemic dynamicsproposed by my collaborator Thilo Gross and his colleagues in 2006 [72]. Theyassumed that the edge from the susceptible to the infected nodes would be rewired toanother susceptible node in order to keep the number of edges constant. But here, weassume that such edges can just be removed for simplicity. For your convenience, Code16.6 for the SIS model is shown below again. Can you see where you can/should implementthis new edge removal assumption in this code?Code 16.13:p_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 infectedg.node[a][’state’] = 0 if random() < p_r else 1There are several different ways to implement adaptive edge removal in this code. Aneasy option would be just to insert another if statement to decide whether to cut the edgeright before simulating the infection of the disease (third-to-last line). For example:Code 16.14:p_i = 0.5 # infection probabilityp_r = 0.5 # recovery probabilityp_s = 0.5 # severance 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 infectedif random() < p_s: