increment = 10000
start = 1000
profile.start_clock()
file = open('csv/sparse_graphs.csv', 'w')
file.write("Nodes GenerateTime SaveTime FileSize\n")
#The +1 on the max size is just to be sure we include the max size in our range.
for i in range(start,max_size+1,increment):
edge_probability = 1.0/i;
scaling = 10
#I only want my graph to be dense with a small probability (average density = 0.03125*#nodes)
G = zen.generating.rgm.erdos_renyi(i,edge_probability*scaling)
G.compact()
filename = 'sparse' + str(i) + ".graph"
#Profiling generation time
difftime = profile.get_time_from_clock()
gentime = str(difftime.seconds) + "." + str(difftime.microseconds/1000)
print "Graph " + filename + " has taken " + gentime + " to generate."
#Saving the generated graph
edgelist.write(G,'storage/edgelist/sparse/' + filename)
filesize = profile.filesize('storage/edgelist/sparse/' + filename)
filesize = filesize/1024
#Profiling IO time
difftime = profile.get_time_from_clock()
savetime = str(difftime.seconds) + "." + str(difftime.microseconds/1000)
print "Graph " + filename + " (" + str(filesize) + "kB), has taken " + savetime + " seconds to save on disk."
file.write(str(i) + " " + gentime + " " + savetime + " " + str(filesize) + "\n")
profile.start_clock()
file = open('csv/random_graphs.csv', 'w')
file.write("Nodes GenerateTime SaveTime FileSize\n")
#The +1 on the max size is just to be sure we include the max size in our range.
for i in range(start,max_size+1,increment):
edge_probability_1 = random.uniform(0,1.0)
edge_probability_2 = random.uniform(0,1.0)
edge_probability_3 = random.uniform(0,1.0)
edge_probability_4 = random.uniform(0,1.0)
edge_probability_5 = random.uniform(0,1.0)
#I only want my graph to be dense with a small probability (average density = 0.03125*#nodes)
G = zen.generating.rgm.erdos_renyi(i,edge_probability_1*edge_probability_2*edge_probability_3*edge_probability_4*edge_probability_5 * 0.1)
G.compact()
filename = 'random' + str(i) + ".graph"
#Profiling generation time
difftime = profile.get_time_from_clock()
gentime = str(difftime.seconds) + "." + str(difftime.microseconds/1000)
print "Graph " + filename + " has taken " + gentime + " to generate."
#Saving the generated graph
edgelist.write(G,'storage/edgelist/random/' + filename)
filesize = profile.filesize('storage/edgelist/random/' + filename)
filesize = filesize/1024
#Profiling IO time
difftime = profile.get_time_from_clock()
savetime = str(difftime.seconds) + "." + str(difftime.microseconds/1000)
print "Graph " + filename + " (" + str(filesize) + "kB), has taken " + savetime + " seconds to save on disk."
file.write(str(i) + " " + gentime + " " + savetime + " " + str(filesize) + "\n")
max_size = 41000
increment = 10000
start = 1000
profile.start_clock()
file = open("csv/barabasi_graphs.csv", "w")
file.write("GraphName GenerateTime SaveTime FileSize\n")
# The +1 on the max size is just to be sure we include the max size in our range.
for i in range(start, max_size + 1, increment):
# Defines the maximum degree per node
G = zen.generating.barabasi_albert(i, 1)
G.compact()
filename = "barabasi" + str(i) + ".graph"
# Profiling generation time
difftime = profile.get_time_from_clock()
gentime = str(difftime.seconds) + "." + str(difftime.microseconds / 1000)
print "Graph " + filename + " has taken " + gentime + " to generate."
# Saving the generated graph
edgelist.write(G, "storage/edgelist/barabasi/" + filename)
filesize = profile.filesize("storage/edgelist/barabasi/" + filename)
filesize = filesize / 1024
# Profiling IO time
difftime = profile.get_time_from_clock()
savetime = str(difftime.seconds) + "." + str(difftime.microseconds / 1000)
print "Graph " + filename + " (" + str(filesize) + "kB), has taken " + savetime + " seconds to save on disk."
file.write(filename + " " + gentime + " " + savetime + " " + str(filesize) + "\n")
#Define edges for each node
for j in range(i):
k = 0
while k < max_degree:
x = random.randint(1,max_degree)
x = round(random.uniform(0,1)*random.uniform(0,1)*x)
other_node = (j+x) % i
if not G.has_edge(j,other_node):
G.add_edge(j,other_node)
k+=1
G.compact()
filename = 'metric' + str(i) + ".graph"
#Profiling generation time
difftime = profile.get_time_from_clock()
gentime = str(difftime.seconds) + "." + str(difftime.microseconds/1000)
print "Graph " + filename + " has taken " + gentime + " to generate."
#Saving the generated graph
edgelist.write(G,'storage/edgelist/metric/' + filename)
filesize = profile.filesize('storage/edgelist/metric/' + filename)
filesize = filesize/1024
#Profiling IO time
difftime = profile.get_time_from_clock()
savetime = str(difftime.seconds) + "." + str(difftime.microseconds/1000)
print "Graph " + filename + " (" + str(filesize) + "kB), has taken " + savetime + " seconds to save on disk."
file.write(str(i) + " " + gentime + " " + savetime + " " + str(filesize) + "\n")
profile.start_clock()
file = open("csv/dense_graphs.csv", "w")
file.write("Nodes GenerateTime SaveTime FileSize\n")
# The +1 on the max size is just to be sure we include the max size in our range.
for i in range(start, max_size + 1, increment):
edge_probability = random.uniform(0, 1.0)
scaling = 0.05
base_value = 0.05
# Graphs of this category are potentially very big (average node degree = 0.0725*#nodes), which means the 20,000 node graph may have up to 4,000,000 edges.
# which would be a graph of size 600MB.
G = zen.generating.rgm.erdos_renyi(i, edge_probability * scaling + base_value)
G.compact()
filename = "dense" + str(i) + ".graph"
# Profiling generation time
difftime = profile.get_time_from_clock()
gentime = str(difftime.seconds) + "." + str(difftime.microseconds / 1000)
print "Graph " + filename + " has taken " + gentime + " to generate."
# Saving the generated graph
edgelist.write(G, "storage/edgelist/dense/" + filename)
filesize = profile.filesize("storage/edgelist/dense/" + filename)
filesize = filesize / 1024
# Profiling IO time
difftime = profile.get_time_from_clock()
savetime = str(difftime.seconds) + "." + str(difftime.microseconds / 1000)
print "Graph " + filename + " (" + str(filesize) + "kB), has taken " + savetime + " seconds to save on disk."
file.write(str(i) + " " + gentime + " " + savetime + " " + str(filesize) + "\n")
