def runExperimentEdges(self, step=5, end=50, mode=0, sname=None):
"""
mode
0 random edge delete
1 random edge rewiring
"""
#cnumber = {0:self.coreNumber(can_cache=True)}
cnumber = {0: nx.core_number(self.graph)}
size = int(self.graph.number_of_edges() * step * 0.01)
if mode == 0:
noise = missing.MissingData()
elif mode == 1:
noise = rewire.RewireEdges()
for i in xrange(1, int(end / step)):
if mode == 0:
self.graph = noise.removeRandomEdges(self.graph, size)
elif mode == 1:
self.graph = noise.rewire(self.graph, size)
cnumber[i * step] = nx.core_number(self.graph)
# if i*step % 10 == 0:
# nx.write_edgelist(self.graph, self.sname + '_' + str(i*step) + '.edgelist', data=False)
return cnumber
def runExperimentNode(self, step=5, end=50):
cnumber = {0: self.coreNumber(can_cache=True)}
noise = missing.MissingData()
size = int(self.graph.number_of_nodes() * step * 0.01)
for i in xrange(1, int(end / step)):
self.graph = noise.removeRandomNodes(self.graph, size)
cnumber[i * step] = self.coreNumber()
return cnumber
def runExperimentNode(self, r=3, s=4, step=10, end=50):
nucleii = {0: self.getNucleii(r, s, can_cache=True)}
noise = missing.MissingData()
size = int(self.graph.number_of_nodes() * step * 0.01)
for i in xrange(1, int(end / step)):
self.graph = noise.removeRandomNodes(self.graph, size)
print(size, 'nodes', self.graph.number_of_nodes())
nucleii[i * step] = self.getNucleii(r, s)
return nucleii
def coreMissing(self, nodes, core_ori):
stop = 80
step = 2
iterer = 10
cnumber = {n: [] for n in nodes}
size = int(self.graph.number_of_edges() * step * 0.01)
noise = missing.MissingData()
header = [
'k_' + str(i * step) for i in xrange(0,
int(stop / step) + 1)
]
for _ in xrange(0, iterer):
self.graph = nx.read_edgelist(self.fname)
for i in xrange(0, int(stop / step)):
self.graph = noise.removeRandomEdges(self.graph, size)
cn = self.coreNumber()
for n in cnumber:
if len(cnumber[n]) < i + 1:
cnumber[n].append([])
if n in cn:
cnumber[n][i].append(cn[n])
else:
cnumber[n][i].append(0)
data = {}
for n in nodes:
data[n] = [core_ori[n]]
for d in cnumber[n]:
data[n].append(np.mean(d))
header += [
'k_diff_' + str(i * step) for i in xrange(1,
int(stop / step) + 1)
]
for n in nodes:
for d in cnumber[n]:
data[n].append(core_ori[n] - np.mean(d))
header += [
'k_diff_ratio_' + str(i * step)
for i in xrange(1,
int(stop / step) + 1)
]
for n in nodes:
for d in cnumber[n]:
data[n].append((core_ori[n] - np.mean(d)) / core_ori[n])
return data, header
def runExperimentEdges(self, r=3, s=4, step=10, end=50, mode=0):
"""
mode
0 random edge delete
1 random edge rewiring
"""
nucleii = {0: self.getNucleii(r, s, can_cache=True)}
size = int(self.graph.number_of_edges() * step * 0.01)
if mode == 0:
noise = missing.MissingData()
elif mode == 1:
noise = rewire.RewireEdges()
for i in xrange(1, int(end / step)):
if mode == 0:
self.graph = noise.removeRandomEdges(self.graph, size)
elif mode == 1:
self.graph = noise.rewire(self.graph, size)
nucleii[i * step] = self.getNucleii(r, s)
return nucleii
def runExperimentEdges(self, step=5, end=50, mode=0):
"""
mode
0 random edge delete
1 random edge rewiring
"""
tnumber = {0: self.trussNumber(can_cache=True)}
size = int(self.graph.number_of_edges() * step * 0.01)
if mode == 0:
noise = missing.MissingData()
elif mode == 1:
noise = rewire.RewireEdges()
for i in xrange(1, int(end / step)):
if mode == 0:
self.graph = noise.removeRandomEdges(self.graph, size)
elif mode == 1:
self.graph = noise.rewire(self.graph, size)
tnumber[i * step] = self.trussNumber()
return tnumber
def removeEdges(graph, pc):
noise = missing.MissingData()
size = int(graph.number_of_edges() * pc * 0.01)
return noise.removeRandomEdges(graph, size)