传播模拟

diffusion_g.py

#coding=utf-8
import diffusion_i as dfi
import numpy as np
import pylab as pl
from igraph import * 
def countN(g,beta,times):
    count = []
    for i in xrange(times):
        result = dfi.diffusion(g=g,beta=beta)
        print('.'),
        count.append(len(result))
    mean = np.mean(count)
    std = np.std(count)
    print "\nmean: %f" % mean
    print "standard deviation: %f" % std 
    return mean, std
if __name__ == "__main__":
    g = Graph.Read_Pajek("facebook_combined.net")
    mean = []
    std = []
    x = np.linspace(0.1,0.5,10)
    for i in xrange(len(x)):
        m, s=countN(g,x[i],10)
        mean.append(m)
        std.append(s)
    pl.subplot(2,1,1)
    pl.plot(x, mean, 'x-.')
    pl.xlim(0.0, 0.55)
    pl.subplot(2,1,2)
    pl.plot(x,std,'ko-')
    pl.xlim(0.0, 0.55)
    pl.show()