Introduction to the Modeling and Analysis of Complex Systems

390CHAPTER 17. DYNAMICAL NETWORKS II: ANALYSIS OF NETWORK TOPOLOGIES(array([ 1., 11., 6., 6., 0., 3., 2., 0., 0., 0., 1., 1., 0., 1., 0.,0., 0., 0., 1., 1.]), array([ 1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8,6.6, 7.4, 8.2, 9. , 9.8, 10.6, 11.4, 12.2, 13. , 13.8, 14.6, 15.4,16.2, 17. ]), )>>> show()The result is shown in Fig. 17.8.1210864200 2 4 6 8 10 12 14 16 18Figure 17.8: Visual output of Code 17.13.You can also obtain the actual degree distribution P (k) as follows:Code 17.14:>>> nx.degree_histogram(g)[0, 1, 11, 6, 6, 3, 2, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1]This list contains the value of (unnormalized) P (k) for k = 0, 1, . . . , k max , in this order. Forlarger networks, it is often more useful to plot a normalized degree histogram list in alog-log scale:Code 17.15: degree-distributions-loglog.pyfrom pylab import *import networkx as nxn = 1000