def main():
msg = "usage: ./p2p2012.py type r|g|g2 ttl par tries churn_rate"
if len(sys.argv) < 7:
print msg
sys.exit(1)
global out_file, churn_rate
out_file = sys.stdout
gtype = sys.argv[1]
walk = sys.argv[2]
ttl = int(sys.argv[3])
par = int(sys.argv[4])
tries = int(sys.argv[5])
churn_rate = float(sys.argv[6])
if gtype == "a":
g = nx.barabasi_albert_graph(97134, 3)
elif gtype == "b":
g = nx.barabasi_albert_graph(905668, 12)
elif gtype == "c":
g = sm.randomWalk_mod(97134, 0.90, 0.23)
elif gtype == "d":
g = sm.randomWalk_mod(905668, 0.93, 0.98)
elif gtype == "e":
g = sm.nearestNeighbor_mod(97134, 0.53, 1)
elif gtype == "f":
g = sm.nearestNeighbor_mod(905668, 0.90, 5)
elif gtype == "g":
g = nx.random_regular_graph(6, 97134)
elif gtype == "h":
g = nx.random_regular_graph(20, 905668)
elif gtype == "i":
g = nx.read_edgelist(sys.argv[7])
if walk == "r":
lookup(g, ttl, tries, par, get_random_node)
elif walk == "g":
lookup(g, ttl, tries, par, get_greedy_node)
elif walk == "g2":
lookup(g, ttl, tries, par, get_greedy2_node)
elif walk == "sum":
sum_edges(g, int(sys.argv[3]))
nodes = g.number_of_nodes()
edges = g.size()
avg_cc = nx.average_clustering(g)
print >> sys.stderr, nodes, edges, avg_cc
def fit_randomWalk_mod(graphSize, graphID, dkPath, original2k, resultPath,
interval):
"""
Runs synthetic graph tests for various 'qe' and 'qv' values
If an interval was specified for fine grained sampling, please
note that the interal is closed bounds, that is [x, y] so qe_end and qv_end
will terminate after sampling the end values specified, not before.
"""
if not interval:
outfile = open(resultPath + graphID + '_rwCoarse_dkDistances.txt', 'w')
qe = 0.1
qe_end = 0.9
step = 0.1
else:
outfile = open(resultPath + graphID + '_rwFine_dkDistances.txt', 'w')
qe = interval[0] * 100
qe_end = interval[1] * 100
step = 1
outfile.write('dk-2 Distance\tqe\tqv\n')
while qe <= qe_end:
if not interval:
qv = 0.1
qv_end = 0.9
else:
qv = interval[2] * 100
qv_end = interval[3] * 100
while qv <= qv_end:
qeFloat = float(qe) / 100 if not interval else qe
qvFloat = float(qv) / 100 if not interval else qv
print 'Running modified Random Walk (coarse) with parameters: n = ', graphSize, ' qe = ', qeFloat, ' qv = ', qvFloat
newFile = graphID + '_rwCoarse_' + str(qeFloat) + '_' + str(
qvFloat)
# Create synthetic graph
syntheticGraph = sm.randomWalk_mod(graphSize, qeFloat, qvFloat)
# Write pickle, edge list, and 2k distro to file
print 'Calculating dK-2...\n'
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(
original2k, resultPath + newFile + '_target.2k')
outfile.write(
str(dkDistance) + '\tqe = ' + str(qeFloat) + '\tqv = ' +
str(qvFloat) + '\n')
outfile.flush()
qv += step
qe += step
outfile.write('\n')
outfile.close()
def fit_randomWalk_mod(graphSize, graphID, dkPath, original2k, resultPath, interval):
"""
Runs synthetic graph tests for various 'qe' and 'qv' values
If an interval was specified for fine grained sampling, please
note that the interal is closed bounds, that is [x, y] so qe_end and qv_end
will terminate after sampling the end values specified, not before.
"""
if not interval:
outfile = open(resultPath + graphID + '_rwCoarse_dkDistances.txt', 'w')
qe = 0.1
qe_end = 0.9
step = 0.1
else:
outfile = open(resultPath + graphID + '_rwFine_dkDistances.txt', 'w')
qe = interval[0] * 100
qe_end = interval[1] * 100
step = 1
outfile.write('dk-2 Distance\tqe\tqv\n')
while qe <= qe_end:
if not interval:
qv = 0.1
qv_end = 0.9
else:
qv = interval[2] * 100
qv_end = interval[3] * 100
while qv <= qv_end:
qeFloat = float(qe) / 100 if not interval else qe
qvFloat = float(qv) / 100 if not interval else qv
print 'Running modified Random Walk (coarse) with parameters: n = ', graphSize, ' qe = ', qeFloat, ' qv = ', qvFloat
newFile = graphID + '_rwCoarse_' + str(qeFloat) + '_' + str(qvFloat)
# Create synthetic graph
syntheticGraph = sm.randomWalk_mod(graphSize, qeFloat, qvFloat)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(original2k, resultPath + newFile + '_target.2k')
outfile.write(str(dkDistance) + '\tqe = ' + str(qeFloat) + '\tqv = ' + str(qvFloat) + '\n')
outfile.flush()
qv += step
qe += step
outfile.write('\n')
outfile.close()
