def modularity(cmatrix, edgetype):
""" Community detection via optimization of modularity
Parameters
----------
cmatrix : adjacency or weights matrix
edgetype : {'undirected', 'directed'}
Returns
-------
edgetype == 'undirected':
Ci : community structure Ci
Q : maximized modularity Q.
Algorithm: Newman's spectral optimization method:
References: Newman (2006) -- Phys Rev E 74:036104; PNAS 23:8577-8582.
Mika Rubinov, UNSW
Modification History:
Jul 2008: Original
Oct 2008: Positive eigenvalues are now insufficient for division
(Jonathan Power, WUSTL)
Dec 2008: Fine-tuning is now consistent with Newman's description
(Jonathan Power)
Dec 2008: Fine-tuning is now vectorized (Mika Rubinov)
edgetype == 'directed':
Ci : community structure Ci
Q : maximized modularity Q.
Algorithm: Newman's spectral optimization method, generalized to
directed networks.
Reference: Leicht and Newman (2008) Phys Rev Lett.
Mika Rubinov, UNSW
Modification History:
Jul 2008: Original
Oct 2008: Positive eigenvalues are now insufficient for division
(Jonathan Power, WUSTL)
Dec 2008: Fine-tuning is now consistent with Newman's description
(Jonathan Power)
Dec 2008: Fine-tuning is now vectorized (Mika Rubinov)
"""
if edgetype == 'undirected':
m = bct.to_gslm(cmatrix.tolist())
strr = bct.modularity_und(m)
bct.gsl_free(m)
return strr
else:
m = bct.to_gslm(cmatrix.tolist())
strr = bct.modularity_dir(m)
bct.gsl_free(m)
return strr
def modularity(cmatrix, edgetype):
""" Community detection via optimization of modularity
Parameters
----------
cmatrix : adjacency or weights matrix
edgetype : {'undirected', 'directed'}
Returns
-------
edgetype == 'undirected':
Ci : community structure Ci
Q : maximized modularity Q.
Algorithm: Newman's spectral optimization method:
References: Newman (2006) -- Phys Rev E 74:036104; PNAS 23:8577-8582.
Mika Rubinov, UNSW
Modification History:
Jul 2008: Original
Oct 2008: Positive eigenvalues are now insufficient for division
(Jonathan Power, WUSTL)
Dec 2008: Fine-tuning is now consistent with Newman's description
(Jonathan Power)
Dec 2008: Fine-tuning is now vectorized (Mika Rubinov)
edgetype == 'directed':
Ci : community structure Ci
Q : maximized modularity Q.
Algorithm: Newman's spectral optimization method, generalized to
directed networks.
Reference: Leicht and Newman (2008) Phys Rev Lett.
Mika Rubinov, UNSW
Modification History:
Jul 2008: Original
Oct 2008: Positive eigenvalues are now insufficient for division
(Jonathan Power, WUSTL)
Dec 2008: Fine-tuning is now consistent with Newman's description
(Jonathan Power)
Dec 2008: Fine-tuning is now vectorized (Mika Rubinov)
"""
if edgetype == 'undirected':
m = bct.to_gslm(cmatrix.tolist())
strr = bct.modularity_und(m)
bct.gsl_free(m)
return strr
else:
m = bct.to_gslm(cmatrix.tolist())
strr = bct.modularity_dir(m)
bct.gsl_free(m)
return strr
def modularity_und(*args): return _bct.modularity_und(*args)
def modularity_und(*args):
return _bct.modularity_und(*args)
def modularity_und(*args): """modularity_und(gsl_matrix A) -> double""" return _bct.modularity_und(*args)
def modularity_und(*args):
"""modularity_und(gsl_matrix A) -> double"""
return _bct.modularity_und(*args)
