def efficiency(cmatrix, local=False, edgetype='undirected', weighted=False):
""" A measure similar to the clustering coefficient, based upon the
calculation of the harmonic mean of neighbor-neighbor distances.
For directed networks, this function works on the out-degree.
A global efficiency matrix is the inverse of the distance matrix
(with self-self distances set to 0). Calculating the global efficiency
is advantageous over distance in disconnected networks:
the efficiency between disconnected pairs of nodes is set to 0
(the inverse of infinity), hence enabling the calculation of network
wide averages (which become meaningless on distance matrices).
Parameters
----------
cmatrix : array_like
Two dimensional connection/adjacency matrix
edgetype : {'undirected'}
weighted : {False}
Returns
-------
local == True:
Eglob : outputs the inverse distance matrix: the mean of this
matrix (excluding main diagonal) is equivalent to the global
efficiency.
local == False:
Eloc : outputs individual nodal local efficiency.
For directed networks, local efficiency works with the
out-degree.
Reference: Latora and Marchiori, 2001, Phys Rev Lett 87:198701.
Algebraic shortest path algorithm.
Mika Rubinov, UNSW, 2008 (last modified September 2008).
"""
if edgetype == 'undirected' and weighted == False:
if local:
m = bct.to_gslm(cmatrix.tolist())
eloc = bct.efficiency_local(m)
elocnp = bct.from_gsl(eloc)
bct.gsl_free(m)
bct.gsl_free(eloc)
return np.asarray(elocnp)
else:
m = bct.to_gslm(cmatrix.tolist())
eloc = bct.efficiency_global(m)
elocnp = bct.from_gsl(eloc)
bct.gsl_free(m)
bct.gsl_free(eloc)
return np.asarray(elocnp)
def efficiency(cmatrix, local = False, edgetype = 'undirected', weighted = False):
""" A measure similar to the clustering coefficient, based upon the
calculation of the harmonic mean of neighbor-neighbor distances.
For directed networks, this function works on the out-degree.
A global efficiency matrix is the inverse of the distance matrix
(with self-self distances set to 0). Calculating the global efficiency
is advantageous over distance in disconnected networks:
the efficiency between disconnected pairs of nodes is set to 0
(the inverse of infinity), hence enabling the calculation of network
wide averages (which become meaningless on distance matrices).
Parameters
----------
cmatrix : array_like
Two dimensional connection/adjacency matrix
edgetype : {'undirected'}
weighted : {False}
Returns
-------
local == True:
Eglob : outputs the inverse distance matrix: the mean of this
matrix (excluding main diagonal) is equivalent to the global
efficiency.
local == False:
Eloc : outputs individual nodal local efficiency.
For directed networks, local efficiency works with the
out-degree.
Reference: Latora and Marchiori, 2001, Phys Rev Lett 87:198701.
Algebraic shortest path algorithm.
Mika Rubinov, UNSW, 2008 (last modified September 2008).
"""
if edgetype == 'undirected' and weighted == False:
if local:
m = bct.to_gslm(cmatrix.tolist())
eloc = bct.efficiency_local(m)
elocnp = bct.from_gsl(eloc)
bct.gsl_free(m)
bct.gsl_free(eloc)
return np.asarray(elocnp)
else:
m = bct.to_gslm(cmatrix.tolist())
eloc = bct.efficiency_global(m)
elocnp = bct.from_gsl(eloc)
bct.gsl_free(m)
bct.gsl_free(eloc)
return np.asarray(elocnp)
def efficiency_local(*args): return _bct.efficiency_local(*args)
def efficiency_local(*args):
return _bct.efficiency_local(*args)
def efficiency_local(*args): """efficiency_local(gsl_matrix G) -> gsl_vector""" return _bct.efficiency_local(*args)
def efficiency_local(*args):
"""efficiency_local(gsl_matrix G) -> gsl_vector"""
return _bct.efficiency_local(*args)