Return the Laplacian matrix of a directed graph.
For nonsymmetric graphs the outdegree is used in the computation.
Parameters :  csgraph: array_like or sparse matrix, 2 dimensions :
normed: bool, optional :
return_diag: bool, optional :


Returns :  lap: ndarray :
diag: ndarray :

Notes
The Laplacian matrix of a graph is sometimes referred to as the “Kirchoff matrix” or the “admittance matrix”, and is useful in many parts of spectral graph theory. In particular, the eigendecomposition of the laplacian matrix can give insight into many properties of the graph.
For nonsymmetric directed graphs, the laplacian is computed using the outdegree of each node.
Examples
>>> from scipy.sparse import csgraph
>>> G = np.arange(5) * np.arange(5)[:, np.newaxis]
>>> G
array([[ 0, 0, 0, 0, 0],
[ 0, 1, 2, 3, 4],
[ 0, 2, 4, 6, 8],
[ 0, 3, 6, 9, 12],
[ 0, 4, 8, 12, 16]])
>>> csgraph.laplacian(G, normed=False)
array([[ 0, 0, 0, 0, 0],
[ 0, 9, 2, 3, 4],
[ 0, 2, 16, 6, 8],
[ 0, 3, 6, 21, 12],
[ 0, 4, 8, 12, 24]])