def question4():
NETWORK_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_rf7.txt"
network_graph = app2.load_graph(NETWORK_URL)
network_idd = prj1.in_degree_distribution(network_graph)
network_nodes = sum(network_idd.values())
network_edges = sum([x * y for (x, y) in network_idd.iteritems()]) / 2
network_prob = float(network_edges) / network_nodes / (network_nodes - 1)
print "Network_nodes", network_nodes
print "Network_edges", network_edges
print "Network_prob", network_prob
print "Network_m", network_edges / float(network_nodes)
er_graph = er_makegraph(1347, 0.00172)
er_idd = prj1.in_degree_distribution(er_graph)
er_nodes = sum(er_idd.values())
er_edges = sum([x * y for (x, y) in er_idd.iteritems()]) / 2
print "ER_nodes", er_nodes
print "ER_edges", er_edges
upa_graph = upa_makegraph(1347, 2)
upa_idd = prj1.in_degree_distribution(upa_graph)
upa_nodes = sum(upa_idd.values())
upa_edges = sum([x * y for (x, y) in upa_idd.iteritems()]) / 2
print "UPA_nodes", upa_nodes
print "UPA_edges", upa_edges
network_resilience = prj2.compute_resilience(
network_graph, fast_targeted_order(network_graph))
er_resilience = prj2.compute_resilience(er_graph,
fast_targeted_order(er_graph))
upa_resilience = prj2.compute_resilience(upa_graph,
fast_targeted_order(upa_graph))
plt.plot(range(len(network_resilience)),
network_resilience,
'-r',
label='Network graph')
plt.plot(range(len(er_resilience)),
er_resilience,
'-b',
label='ER-generated (p = 0.00172)')
plt.plot(range(len(upa_resilience)),
upa_resilience,
'-g',
label='UPA-generated (m = 2)')
plt.title('Comparison of Networks Resilience')
plt.ylabel('Size of the largest connected component')
plt.xlabel('Number of nodes removed')
plt.legend()
plt.show()
#question4()
def question4():
NETWORK_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_rf7.txt"
network_graph = app2.load_graph(NETWORK_URL)
network_idd = prj1.in_degree_distribution(network_graph)
network_nodes = sum(network_idd.values())
network_edges = sum([x * y for (x, y) in network_idd.iteritems()]) / 2
network_prob = float(network_edges) / network_nodes / (network_nodes - 1)
print "Network_nodes", network_nodes
print "Network_edges", network_edges
print "Network_prob", network_prob
print "Network_m", network_edges /float(network_nodes)
er_graph = er_makegraph(1347, 0.00172)
er_idd = prj1.in_degree_distribution(er_graph)
er_nodes = sum(er_idd.values())
er_edges = sum([x * y for (x, y) in er_idd.iteritems()]) / 2
print "ER_nodes", er_nodes
print "ER_edges", er_edges
upa_graph = upa_makegraph(1347, 2)
upa_idd = prj1.in_degree_distribution(upa_graph)
upa_nodes = sum(upa_idd.values())
upa_edges = sum([x * y for (x, y) in upa_idd.iteritems()]) / 2
print "UPA_nodes", upa_nodes
print "UPA_edges", upa_edges
network_resilience = prj2.compute_resilience(network_graph, fast_targeted_order(network_graph))
er_resilience = prj2.compute_resilience(er_graph, fast_targeted_order(er_graph))
upa_resilience = prj2.compute_resilience(upa_graph, fast_targeted_order(upa_graph))
plt.plot(range(len(network_resilience)), network_resilience, '-r', label='Network graph')
plt.plot(range(len(er_resilience)), er_resilience, '-b', label='ER-generated (p = 0.00172)')
plt.plot(range(len(upa_resilience)), upa_resilience, '-g', label='UPA-generated (m = 2)')
plt.title('Comparison of Networks Resilience')
plt.ylabel('Size of the largest connected component')
plt.xlabel('Number of nodes removed')
plt.legend()
plt.show()
#question4()
def upa_makegraph(num_nodes, iter_nodes):
"""
Generates graph by UPA algorithm where num_nodes is a total number of nodes
and iter_nodes is a number of edges added on each iteration
"""
result = prj1.make_complete_graph(iter_nodes)
upa = UPATrial(iter_nodes)
if num_nodes <= iter_nodes:
return result
for step in range(iter_nodes, num_nodes):
nodes_to_be_added = upa.run_trial(iter_nodes)
result[step] = nodes_to_be_added
for node in nodes_to_be_added:
result[node].add(step)
return result
def upa_makegraph(num_nodes, iter_nodes):
"""
Generates graph by UPA algorithm where num_nodes is a total number of nodes
and iter_nodes is a number of edges added on each iteration
"""
result = prj1.make_complete_graph(iter_nodes)
upa = UPATrial(iter_nodes)
if num_nodes <= iter_nodes:
return result
for step in range(iter_nodes, num_nodes):
nodes_to_be_added = upa.run_trial(iter_nodes)
result[step] = nodes_to_be_added
for node in nodes_to_be_added:
result[node].add(step)
return result
