def compute_cluster_coefficient(dataset):
n = int(nsd.compute_node_number(dataset))
d = {}
for x in xrange(n+1):
d[x] = []
cc = []
with open(dataset, 'r') as f:
for line in f.readlines():
i, j = [int(x) for x in line.strip().split(' ')]
d[i].append(j)
d[j].append(i)
nv = 0
for k, v in d.iteritems():
if v == []:
cc.append('Undefined') # cc of node without connection should be undefined
elif len(v) == 1:
cc.append(0.0)
else:
for u1 in v:
for u2 in d[u1]:
if u2 in v:
nv += 1
nv = nv / 2.0 # (u1, u2) and (u2, u1) should be same, my algorithm counts twice,so here merge them
cc.append((2 * nv)/ (len(v) * (len(v) - 1) )) #
nv = 0
return cc
def compute_triangle_number(dataset):
n = int(nsd.compute_node_number(dataset))
d = {}
for x in xrange(n+1):
d[x] = []
with open(dataset, 'r') as f:
for line in f.readlines():
i, j = [int(x) for x in line.strip().split(' ')]
d[i].append(j)
d[j].append(i)
nv = 0
ntr = 0
for k, v in d.iteritems():
if v == []:
ntr += 0
elif len(v) == 1:
ntr += 0
else:
ntr += nCr(len(v), 2)
for u1 in v:
for u2 in d[u1]:
if u2 in v:
nv += 1
nv= nv / 2.0 # (u1, u2, u3) and (u2, u1, u3) and (u3, u2, u1) should be same, my algorithm counts triple,so here merge them
return nv / ntr
def array_list(dataset):
n = int(nsd.compute_node_number(dataset))
d = {}
for x in xrange(n+1):
d[x] = []
with open(dataset, 'r') as f:
for line in f.readlines():
i, j = [int(x) for x in line.strip().split(' ')]
d[i].append(j)
d[j].append(i)
return d
