def main():
citation_graph = load_citation_graph()
in_degress_dis = in_degree_distribution(citation_graph)
plot_size = sum(in_degress_dis.values())
# normalize the distribution
# (make the values in the dictionary sum to one)
for in_degress in in_degress_dis:
in_degress_dis[in_degress] /= float(plot_size)
plot = []
# for input_val in range(plot_size):
for input_val in range(2, plot_size):
if not in_degress_dis.get(input_val):
continue
counter = in_degress_dis[input_val]
plot.append([input_val, counter])
plot_x = lambda xy: [x[0] for x in xy]
plot_y = lambda xy: [x[1] for x in xy]
plt.loglog(plot_x(plot), plot_y(plot))
plt.xlabel("Input")
plt.ylabel("Counter")
plt.title("Iteration counts")
plt.grid(True)
plt.show()
def main():
# pp = [0.45, 0.5, 0.55]
pp = [0.55]
ns = 1000
for p in pp:
citation_graph = Alg_ER(ns, p)
in_degress_dis = in_degree_distribution(citation_graph)
plot_size = sum(in_degress_dis.values())
# normalize the distribution
# (make the values in the dictionary sum to one)
for in_degress in in_degress_dis:
in_degress_dis[in_degress]/=float(plot_size)
plot = []
# for input_val in range(plot_size):
for input_val in range(2, plot_size):
if not in_degress_dis.get(input_val): continue
counter = in_degress_dis[input_val]
plot.append([input_val, counter])
plot_x = lambda xy: [x[0] for x in xy]
plot_y = lambda xy: [x[1] for x in xy]
# plot.append([math.log(input_val), math.log(counter)])
plot2 = []
for input_val, counter in plot:
plot2.append([math.log(input_val), math.log(counter)])
# plt.loglog(plot_x(plot), plot_y(plot), color = 'r')
plt.plot(plot_x(plot2), plot_y(plot2), color = 'r')
plt.plot(plot_x(plot), plot_y(plot), color = 'g')
plt.xlabel('Input')
plt.ylabel('Counter')
plt.title('Nodes(%d), P(%.2f)' % (ns, p))
plt.grid(True)
plt.show()
