Comments (3)
'''
Participation coefficient is a measure of diversity of intermodular
connections of individual nodes.
Parameters
----------
W : NxN np.ndarray
binary/weighted directed/undirected connection scipy.sparse.csr matrix
ci : Nx1 np.ndarray
community affiliation vector
degree : str
Flag to describe nature of graph 'undirected': For undirected graphs
'in': Uses the in-degree
'out': Uses the out-degree
Returns
-------
P : Nx1 np.ndarray
participation coefficient
'''
if degree == 'in':
W = W.T
_, ci = np.unique(ci, return_inverse=True)
ci += 1
n = W.shape[0] # number of vertices
Ko = np.array(W.sum(axis=1)).flatten().astype(float) # (out) degree
Gc = np.dot((W.toarray() != 0), np.diag(ci)) # neighbor community affiliation
Gc = sparse.csr_matrix(Gc)
P = np.zeros((n)) # community-specific neighbors
for i in range(1, int(np.max(ci)) + 1):
P = P + (np.array((W.multiply(Gc == i).astype(int)).sum(axis=1)).flatten() / Ko)**2
P = 1 - P
# P=0 if for nodes with no (out) neighbors
P[np.where(np.logical_not(Ko))] = 0
from bctpy.
I think this works for all cases. I didn't test a directed graph, though.
from bctpy.
from bctpy.
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from bctpy.