def graph_laplacian(csgraph, normed=False, return_diag=False):
 if ((csgraph.ndim != 2) or (csgraph.shape[0] != csgraph.shape[1])):
  raise ValueError('csgraph must be a square matrix or array')
 if (normed and (np.issubdtype(csgraph.dtype, np.int) or np.issubdtype(csgraph.dtype, np.uint))):
  csgraph = check_array(csgraph, dtype=np.float64, accept_sparse=True)
 if sparse.isspmatrix(csgraph):
  return _laplacian_sparse(csgraph, normed=normed, return_diag=return_diag)
 else:
  return _laplacian_dense(csgraph, normed=normed, return_diag=return_diag)

def graph_laplacian ( csgraph , normed = False , return_diag = False ) : if ( ( csgraph . ndim != 2 ) or ( csgraph . shape [ 0 ] != csgraph . shape [ 1 ] ) ) : raise ValueError ( 'csgraph must be a square matrix or array' ) if ( normed and ( np . issubdtype ( csgraph . dtype , np . int ) or np . issubdtype ( csgraph . dtype , np . uint ) ) ) : csgraph = check_array ( csgraph , dtype = np . float64 , accept_sparse = True ) if sparse . isspmatrix ( csgraph ) : return _laplacian_sparse ( csgraph , normed = normed , return_diag = return_diag ) else : return _laplacian_dense ( csgraph , normed = normed , return_diag = return_diag )
return the laplacian matrix of a directed graph .