sparse matrices about bctpy HOT 3 OPEN

On Tue, Sep 25, 2018, 10:30 AM Maxwell Bertolero ***@***.***> wrote: ''' 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 — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#59 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAk9HrKyd8acAYnBcB2zFfz_m07z9mv8ks5uej32gaJpZM4W3Qz8> .