def generate_GNE(n, m):
ak = math.floor(2 * m / n)
dm = 2 * m - ak * n
g = random_graph(n,
lambda i: ak + 1 if i < dm else ak,
directed=False,
random=False)
random_rewire(g, model='erdos', n_iter=10, edge_sweep=True)
return g
def rewire_dataset_partially(orig_dataset_name, new_dataset_name, p=0.1):
graphs, prop_maps = read_dataset(orig_dataset_name)
for g in graphs:
pins = g.new_edge_property('int')
for e in g.edges():
if random.random() < p:
pins[e] = 0
else:
pins[e] = 1
random_rewire(g, model='erdos', n_iter=10, edge_sweep=True, pin=pins)
write_modified_dataset(orig_dataset_name, new_dataset_name, graphs,
prop_maps)
def opt(g,model="uncorrelated"):
h = gt.Graph(g)
diam, aspl = diam_aspl(h)
while True:
print(diam,aspl)
tmp = gt.Graph(h)
print(random_rewire(tmp,model=model))
tmp_diam, tmp_aspl = diam_aspl(tmp)
if tmp_diam < diam or ( tmp_diam == diam and tmp_aspl < aspl ):
h, diam, aspl = tmp, tmp_diam, tmp_aspl
def opt(g, model="uncorrelated"):
h = gt.Graph(g)
diam, aspl = diam_aspl(h)
while True:
print(diam, aspl)
tmp = gt.Graph(h)
print(random_rewire(tmp, model=model))
tmp_diam, tmp_aspl = diam_aspl(tmp)
if tmp_diam < diam or (tmp_diam == diam and tmp_aspl < aspl):
h, diam, aspl = tmp, tmp_diam, tmp_aspl
def gnp(n, e):
'''
Return an Erdos-renyi graph with n vertices and e edges
'''
g = Graph(directed=False)
g.add_vertex(n)
edgesAdded = 0
edgesNeeded = True
while edgesNeeded:
fromNode = range(n); toNode = range(n);
np.random.shuffle(fromNode)
np.random.shuffle(toNode)
for i in xrange(n):
if fromNode[i] == toNode[i] or g.edge(fromNode[i], toNode[i]) != None: continue #No self-loops or parallel edges
g.add_edge(fromNode[i], toNode[i])
edgesAdded += 1
if edgesAdded >= e:
edgesNeeded = False
break
generation.random_rewire(g, model="erdos")
return g
def rewire_dataset_with_preserved_degree(orig_dataset_name, new_dataset_name):
graphs, prop_maps = read_dataset(orig_dataset_name)
for g in graphs:
random_rewire(g, model='configuration', n_iter=10, edge_sweep=True)
write_modified_dataset(orig_dataset_name, new_dataset_name, graphs,
prop_maps)