# scipy.sparse.csgraph.laplacian

scipy.sparse.csgraph.laplacian(csgraph, normed=False, return_diag=False, use_out_degree=False)

  Return the Laplacian matrix of a directed graph.

  Parameters
    csgraph array_like or sparse matrix, 2 dimensions

      compressed-sparse graph, with shape (N, N).

    normed bool, optional

      If True, then compute symmetric normalized Laplacian.

    return_diag bool, optional

      If True, then also return an array related to vertex degrees.

    use_out_degree bool, optional

      If True, then use out-degree instead of in-degree. This distinction matters only if the graph is asymmetric. Default: False.

  Returns
    lap ndarray or sparse matrix

      The N x N laplacian matrix of csgraph. It will be a NumPy array (dense) if the input was dense, or a sparse matrix otherwise.

    diag ndarray, optional

      The length-N diagonal of the Laplacian matrix. For the normalized Laplacian, this is the array of square roots of vertex degrees or 1 if the degree is zero.

  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 eigen-decomposition of the laplacian matrix can give insight into many properties of the graph.

  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]])

scipy.sparse.csgraph.connected_components scipy.sparse.csgraph.shortest_path