def approx_neighborhood_function_statistics(G,
n_nodes,
n_approx=64,
approx_type=APPROX_BFS_IGRAPH):
aG = convert_networkx_to_SNAP(G)
print "convert to SNAP graph: DONE"
# TEST (fixed)
# s_Diam = 20 # youtube
# diameter s_Diam (lowerbound)
start = time.clock()
if approx_type == APPROX_ANF:
s_Diam = sn.GetAnfEffDiam(aG, False, 0.99, n_approx)
elif approx_type == APPROX_BFS:
s_Diam = sn.GetBfsFullDiam(aG, N_BFS, False) #
elif approx_type == APPROX_BFS_IGRAPH:
s_APD_i, s_EDiam_i, s_CL_i, s_Diam = bfs_samples(G) # _i: igraph
else:
print "Wrong <approx_type> !"
print "compute s_Diam, elapsed :", time.clock() - start
# average distance s_APD
DistNbrsV = sn.TIntFltKdV()
MxDist = int(math.ceil(s_Diam))
print "MxDist =", MxDist
start = time.clock()
sn.GetAnf(aG, DistNbrsV, MxDist, False, n_approx) # n_approx=32, 64...
print "GetAnf, elapsed :", time.clock() - start
# for item in DistNbrsV:
# print item.Key(), "-", item.Dat()
sum_APD = 0.0
dist_list = [] # list of pairs
for item in DistNbrsV:
dist_list.append([item.Key(), item.Dat()])
num_APD = dist_list[-1][1]
# WAY 2 - compute s_EDiam from dist_list
s_EDiam = 0
for i in range(len(dist_list)):
if dist_list[i][1] >= 0.9 * num_APD:
s_EDiam = dist_list[i][0]
break
for i in range(len(dist_list) - 1, 1,
-1): # do not subtract [0] from [1] !
dist_list[i][1] = dist_list[i][1] - dist_list[i - 1][
1] # compute differences
sum_APD += dist_list[i][0] * dist_list[i][1]
s_APD = sum_APD / num_APD
print "num_APD =", num_APD
# for s_PDD
print "s_PDD :"
d_list = []
for item in dist_list:
# print item[0], "-", item[1]
d_list.append(item[1])
print d_list
# WAY 1 - effective diameter s_EDiam ( SNAP)
# start = time.clock()
# if approx_type == APPROX_ANF:
# s_EDiam = sn.GetAnfEffDiam(aG, False, 0.9, n_approx) # 90%
# elif approx_type == APPROX_BFS:
# s_EDiam = sn.GetBfsEffDiam(aG, 1000, False)
# else:
# print "Wrong <approx_type> !"
# print "compute s_EDiam, elapsed :", time.clock() - start
# connectivity length s_CL
sum_CL = 0.0
for item in dist_list:
if item[0] > 0:
sum_CL += item[1] / item[0]
# s_CL = n_nodes*(n_nodes-1)/sum_CL
s_CL = num_APD / sum_CL
#
return s_APD, float(s_EDiam), s_CL, float(s_Diam), s_APD_i, float(
s_EDiam_i), s_CL_i, dist_list
'''
Created on Mar 24, 2014
@author: huunguye
'''
import snap
##################
Graph = snap.GenRndGnm(snap.PNGraph, 100, 1000)
SrcNId = 0
DistNbrsV = snap.TIntFltKdV()
snap.GetAnf(Graph, SrcNId, DistNbrsV, 3, False, 32)
for item in DistNbrsV:
print item.Dat()
print "DONE"
UGraph = snap.GenRndGnm(snap.PUNGraph, 100, 1000)
SrcNId = 0
DistNbrsV = snap.TIntFltKdV()
snap.GetAnf(UGraph, SrcNId, DistNbrsV, 3, False, 32)
for item in DistNbrsV:
print item.Dat()
print "DONE"
Network = snap.GenRndGnm(snap.PNEANet, 100, 1000)
SrcNId = 0
DistNbrsV = snap.TIntFltKdV()
snap.GetAnf(Network, SrcNId, DistNbrsV, 3, False, 32)
for item in DistNbrsV:
def get_approximate_neighborhood(G, n, max_hops, directed=False, approx=32):
DistNbrsV = snap.TIntFltKdV()
snap.GetAnf(G, n, DistNbrsV, max_hops, directed, approx)
def hopSNAP( graph, maxnum = 10, qual = 1024):
DistNbrsV = snap.TIntFltKdV()
snap.GetAnf(graph, DistNbrsV, maxnum, False, qual) # adjust the approximation quality if too slow
cnt = [ dn.Dat() for dn in DistNbrsV ]
hop = [ dn.Key() for dn in DistNbrsV ]
return [ hop, cnt ]