def pagerank(adj_csr, tol=0):
N = adj_csr.shape[0]
# Get transition matrix
T = atMatrix.toRightStochasticCSR(adj_csr)
# Get First Eigen-Vector
x = np.matrix(np.ones((N, )) * 1. / N)
xi = x * T
k = 1
eps = np.abs(xi - x).sum()
while eps > tol:
x = xi.copy()
xi = x * T
k += 1
eps = np.abs(xi - x).sum()
return np.array(xi)
def pagerank(adj_csr, tol=0):
N = adj_csr.shape[0]
# Get transition matrix
T = atMatrix.toRightStochasticCSR(adj_csr)
# Get First Eigen-Vector
x = np.matrix(np.ones((N,)) * 1. / N)
xi = x * T
k = 1
eps = np.abs(xi - x).sum()
while eps > tol:
x = xi.copy()
xi = x * T
k += 1
eps = np.abs(xi - x).sum()
return np.array(xi)
def com2comTrans(T, member):
# Get the flow matrix of the module partition from
# the PageRank, Degree and membership of its nodes
coms = np.unique(member)
ncom = coms.shape[0]
com2com = np.zeros((ncom, ncom))
if sparse.issparse(T):
for ii in range(ncom):
iiCom = member == coms[ii]
Tii = T[iiCom]
if T.format == 'csr':
rowIndices = Tii.indices
# elif T.format == 'lil':
# rowIndices = Tii.rows[0]
else:
sys.exit('Type %s of sparse matrix not implemented.' % T.format)
for k in range(Tii.nnz):
jj = rowIndices[k]
com2com[ii, np.nonzero(coms == member[jj])] += Tii.data[k]
com2com = atMatrix.toRightStochasticCSR(sparse.csr_matrix(com2com))
return com2com
def com2comTrans(T, member):
# Get the flow matrix of the module partition from
# the PageRank, Degree and membership of its nodes
coms = np.unique(member)
ncom = coms.shape[0]
com2com = np.zeros((ncom, ncom))
if sparse.issparse(T):
for ii in range(ncom):
iiCom = member == coms[ii]
Tii = T[iiCom]
if T.format == 'csr':
rowIndices = Tii.indices
# elif T.format == 'lil':
# rowIndices = Tii.rows[0]
else:
sys.exit('Type %s of sparse matrix not implemented.' %
T.format)
for k in range(Tii.nnz):
jj = rowIndices[k]
com2com[ii, np.nonzero(coms == member[jj])] += Tii.data[k]
com2com = atMatrix.toRightStochasticCSR(sparse.csr_matrix(com2com))
return com2com
