def test_clustercoefficient():
ps = np.arange(0, 1, 0.01)
n = 1000
e = 10
labels = string.ascii_lowercase + string.ascii_uppercase
vs = []
iter = misc.gen_identifier()
for i in range(n):
vs.append(Vertex(iter.next()))
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
c0 = g.get_clustering_coefficient()
xs = []
ys = []
for p in ps:
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
g.rewire(p)
ys.append(g.get_clustering_coefficient() / c0)
xs.append(p)
fig = plt.figure(dpi = 100)
plt.subplot(1,1,1)
plt.plot(xs, ys)
plt.xscale('log')
plt.show()
def test_average_path_len():
ps = np.arange(0, 1, 0.05)
print ps
n = 1000
e = 10
labels = string.ascii_lowercase + string.ascii_uppercase
vs = []
iter = misc.gen_identifier()
for i in range(n):
vs.append(Vertex(iter.next()))
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
l0 = g.get_averaged_shortest_path()
layout = CircleLayout(g)
xs = []
ys = []
for p in ps:
g = SmallWorldGraph(vs)
g.add_regular_ring_lattice(e)
g.rewire(p)
l1 = g.get_averaged_shortest_path() / l0
ys.append(l1)
xs.append(p)
print l1, p
fig = plt.figure(dpi = 100)
plt.subplot(1,1,1)
plt.plot(xs, ys)
plt.xscale('log')
plt.show()
def makeGraph(n='52', k='5', p='0.1'):
#create n Vertices
n = int(n)
k = int(k)
p = float(p)
names = name_generator()
vs = [Vertex(names.next()) for c in range(n)]
# create a graph
g = SmallWorldGraph(vs, k, p)
start = clock()
g.char_length()
charLength1 = clock() - start
start = clock()
g.char_length2()
charLength2 = clock() - start
start = clock()
g.char_length3()
charLength3 = clock() - start
start = clock()
g.char_length4()
charLength4 = clock() - start
return charLength1, charLength2, charLength3, charLength4
def generateData(pStart, pMultiplier):
c0 = 0.75
x = []
y = []
while (pStart < 1):
names = name_generator()
vs = [Vertex(names.next()) for c in range(52)]
graph = SmallWorldGraph(vs, 5, pStart)
x.append(pStart)
y.append(graph.cluster_coef() / c0)
pStart *= pMultiplier
return [x, y]
def generateData(pStart, pMultiplier):
l0 = (52 / (2 * 5))
x = []
y = []
while (pStart < 1):
names = name_generator()
vs = [Vertex(names.next()) for c in range(52)]
graph = SmallWorldGraph(vs, 5, pStart)
x.append(pStart)
y.append(graph.char_length() / l0)
pStart *= pMultiplier
return [x, y]
def main(n='20', k='4', p='0.4'):
n = int(n)
k = int(k)
p = float(p)
g = SmallWorldGraph(n, k)
g.rewire(p)
layout = CircleLayout(g)
# draw the graph
gw = GraphWorld()
gw.show_graph(g, layout)
gw.mainloop()
def main(script, n='25', *args):
# create n Vertices
n = int(n)
labels = string.ascii_lowercase + string.ascii_uppercase
vs = [Node(c) for c in labels[:n]]
# create a graph and a layout
g = SmallWorldGraph(vs)
# g.add_random_edges(.2)
# g.add_all_edges()
g.add_regular_edges(4)
g.rewire(.20)
layout = CircleLayout(g)
# draw the graph
gw = GraphWorld()
gw.show_graph(g, layout)
gw.mainloop()
def main(script, n='10', *args): # create n Vertices n = int(n) labels = string.ascii_lowercase + string.ascii_uppercase vs = [] iter = misc.gen_identifier() for i in range(n): vs.append(Vertex(iter.next())) vs = [Vertex(c) for c in labels[:n]] #test_average_path_len() # create a graph and a layout #g = Graph(vs) #g = RandomGraph(vs) #g.is_connected g = SmallWorldGraph(vs) g.add_regular_ring_lattice(4) print g.all_pairs_shortest_path() #v = vs[0] #print v #print g.shortest_path(v) #w = vs[3] #print w #print g.shortest_path(v, w) #print g.get_max_neighbors() #print g.get_clustering_coefficient() #p = 0.8 #g.rewire(p) #print g.get_clustering_coefficient() #test_clustercoefficient() #g.add_random_edges(1.0) #print g.is_connected() # draw the graph layout = CircleLayout(g) gw = GraphWorld() gw.show_graph(g, layout) gw.mainloop()
def main(script, n='25', *args):
# create n Vertices
n = int(n)
labels = string.ascii_lowercase + string.ascii_uppercase
vs = [Node(c) for c in labels[:n]]
# create a graph and a layout
g = SmallWorldGraph(vs)
# g.add_random_edges(.2)
# g.add_all_edges()
g.add_regular_edges(4)
g.rewire(.20)
layout = CircleLayout(g)
# draw the graph
gw = GraphWorld()
gw.show_graph(g, layout)
gw.mainloop()
def main(script, n='1000', k='10', *args):
n = int(n) #number of vertices
k = int(k) #number of edges in regular graph
vertices = [Vertex(c) for c in range(n)]
g = SmallWorldGraph(vertices)
g.add_regular_edges(k)
#regular graph's clustering coefficient and avg path length
c_zero = g.clustering_coefficient()
l_zero = g.average_path_length()
print c_zero, l_zero
f = open("plots/output.csv", "wb")
print 'p\tC\tL'
f.write('p,C(p)/C(0),L(p)/L(0)\n')
#begin rewiring to tease out small-world network characteristics
for log_exp in numpy.arange(-40, 1, 1): #incrementation scheme for logarithmic exponents
g = SmallWorldGraph(vertices)
g.add_regular_edges(k)
p = numpy.power(10, log_exp/10.0)
g.rewire(p)
print '%s\t%s\t%s' % (p, g.clustering_coefficient()/c_zero, g.average_path_length()/l_zero)
f.write('%s,%s,%s\n' % (p, g.clustering_coefficient()/c_zero, g.average_path_length()/l_zero))
f.close()
