def betweenness(cmatrix, weighted=False):
""" Betweenness centrality
Only for directed networks.
Parameters
----------
cmatrix : connection/adjacency matrix
weighted : {False, True}
Returns
-------
weighted == True:
BC : Betweenness centrality BC for weighted directed graph
The input matrix must be a mapping from weight to distance (eg. higher
correlations may be interpreted as short distances - hence an inverse
mapping is appropriate in that case).
Betweenness may be normalised to [0,1] via BC/[(N-1)(N-2)]
Brandes's modified Dijkstra's algorithm; J Math Sociol (2001)
25:163-177.
Mika Rubinov, UNSW, 2007 (last modified July 2008)
weighted == False:
BC : betweenness centrality BC, for a binary directed graph G
Betweenness may be normalised to [0,1] via BC/[(N-1)(N-2)]
Algorithm of Kintali, generalised to directed and disconnected graphs
http://www.cc.gatech.edu/~kintali/papers/bc.pdf
Mika Rubinov, UNSW, 2007 (last modified July 2008)
"""
if weighted:
m = bct.to_gslm(cmatrix.tolist())
dist = bct.betweenness_wei(m)
distnp = bct.from_gsl(dist)
bct.gsl_free(m)
bct.gsl_free(dist)
return np.asarray(distnp)
else:
m = bct.to_gslm(cmatrix.tolist())
dist = bct.betweenness_bin(m)
distnp = bct.from_gsl(dist)
bct.gsl_free(m)
bct.gsl_free(dist)
return np.asarray(distnp)
def betweenness(cmatrix, weighted = False):
""" Betweenness centrality
Only for directed networks.
Parameters
----------
cmatrix : connection/adjacency matrix
weighted : {False, True}
Returns
-------
weighted == True:
BC : Betweenness centrality BC for weighted directed graph
The input matrix must be a mapping from weight to distance (eg. higher
correlations may be interpreted as short distances - hence an inverse
mapping is appropriate in that case).
Betweenness may be normalised to [0,1] via BC/[(N-1)(N-2)]
Brandes's modified Dijkstra's algorithm; J Math Sociol (2001)
25:163-177.
Mika Rubinov, UNSW, 2007 (last modified July 2008)
weighted == False:
BC : betweenness centrality BC, for a binary directed graph G
Betweenness may be normalised to [0,1] via BC/[(N-1)(N-2)]
Algorithm of Kintali, generalised to directed and disconnected graphs
http://www.cc.gatech.edu/~kintali/papers/bc.pdf
Mika Rubinov, UNSW, 2007 (last modified July 2008)
"""
if weighted:
m = bct.to_gslm(cmatrix.tolist())
dist = bct.betweenness_wei(m)
distnp = bct.from_gsl(dist)
bct.gsl_free(m)
bct.gsl_free(dist)
return np.asarray(distnp)
else:
m = bct.to_gslm(cmatrix.tolist())
dist = bct.betweenness_bin(m)
distnp = bct.from_gsl(dist)
bct.gsl_free(m)
bct.gsl_free(dist)
return np.asarray(distnp)
def betweenness_wei(*args): return _bct.betweenness_wei(*args)
def betweenness_wei(*args):
return _bct.betweenness_wei(*args)
def betweenness_wei(*args): """betweenness_wei(gsl_matrix G) -> gsl_vector""" return _bct.betweenness_wei(*args)
def betweenness_wei(*args):
"""betweenness_wei(gsl_matrix G) -> gsl_vector"""
return _bct.betweenness_wei(*args)
