def largest_connected_set(C, directed=True):
    r"""Largest connected component for a directed graph with edge-weights
    given by the count matrix.

    Parameters
    ----------
    C : scipy.sparse matrix
        Count matrix specifying edge weights.
    directed : bool, optional
       Whether to compute connected components for a directed  or
       undirected graph. Default is True.

    Returns
    -------
    lcc : array of integers
        The largest connected component of the directed graph.

    See also
    --------
    connected_sets

    Notes
    -----
    Viewing the count matrix as the adjacency matrix of a (directed)
    graph the largest connected set is the largest connected set of
    nodes of the corresponding graph. The largest connected set of a graph
    can be efficiently computed using Tarjan's algorithm.

    References
    ----------
    .. [1] Tarjan, R E. 1972. Depth-first search and linear graph
        algorithms. SIAM Journal on Computing 1 (2): 146-160.

    Examples
    --------

    >>> import numpy as np
    >>> from msmtools.estimation import largest_connected_set

    >>> C =  np.array([[10, 1, 0], [2, 0, 3], [0, 0, 4]])
    >>> lcc_directed = largest_connected_set(C)
    >>> lcc_directed
    array([0, 1])

    >>> lcc_undirected = largest_connected_set(C, directed=False)
    >>> lcc_undirected
    array([0, 1, 2])

    """
    if isdense(C):
        return sparse.connectivity.largest_connected_set(csr_matrix(C), directed=directed)
    else:
        return sparse.connectivity.largest_connected_set(C, directed=directed)