def undirected(graph, scaled=False):
A = get_matrix(graph).toarray()
n = len(A)
I = identity(n)
ones = array([1 for _ in range(n)])
beta = 1. / (n - 1.)
h_star = dot(inv(I + beta * A), ones)
if not scaled:
return h_star
return scale_centrality(h_star)
def outgoing(graph, scaled=False):
A = get_matrix(graph).toarray()
n = len(A)
I = identity(n)
ones = array([1 for _ in range(n)])
beta1 = 1. / (n - 1.)
beta2 = 1. / ((n - 1.)**2)
h_star = dot(
dot(inv(I - beta2 * dot(A, transpose(A))), (I - beta1 * A)), ones)
if not scaled:
return h_star
return scale_centrality(h_star)
def undirected_on_matrices(positive_matrix, negative_matrix, scaled=False):
P = positive_matrix # Positive weights
N = negative_matrix # Negative weights
A = P - 2. * N # All weights
n = len(A)
I = identity(n)
ones = array([1 for _ in range(n)])
beta = 1. / (2. * n - 2.)
PN = dot(inv(I - beta * A), ones)
if not scaled:
return PN
return scale_centrality(PN)
def outgoing_on_matrices(positive_matrix, negative_matrix, scaled=False):
P = positive_matrix # Positive weights
N = negative_matrix # Negative weights
A = P - 2. * N # All weights
n = len(A)
I = identity(n)
ones = array([1 for _ in range(n)])
beta1 = 1. / (4 * ((n - 1.)**2))
beta2 = 1. / (2 * (n - 1.))
PN = dot(dot(inv(I - beta1 * dot(A, transpose(A))), (I + beta2 * A)),
ones)
if not scaled:
return PN
return scale_centrality(PN)