def METHOD_NAME(G, normalized=True, Q=100, seed=None):
    r"""Returns the rich-club coefficient of the graph `G`.
    For each degree *k*, the *rich-club coefficient* is the ratio of the
    number of actual to the number of potential edges for nodes with
    degree greater than *k*:
    .. math::
        \phi(k) = \frac{2 E_k}{N_k (N_k - 1)}
    where `N_k` is the number of nodes with degree larger than *k*, and
    `E_k` is the number of edges among those nodes.
    Parameters
    ----------
    G : NetworkX graph
        Undirected graph with neither parallel edges nor self-loops.
    normalized : bool (optional)
        Normalize using randomized network as in [1]_
    Q : float (optional, default=100)
        If `normalized` is True, perform `Q * m` double-edge
        swaps, where `m` is the number of edges in `G`, to use as a
        null-model for normalization.
    seed : integer, random_state, or None (default)
        Indicator of random number generation state.
        See :ref:`Randomness<randomness>`.
    Returns
    -------
    rc : dictionary
       A dictionary, keyed by degree, with rich-club coefficient values.
    Examples
    --------
    >>> G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5)])
    >>> rc = nx.rich_club_coefficient(G, normalized=False, seed=42)
    >>> rc[0]
    0.4
    Notes
    -----
    The rich club definition and algorithm are found in [1]_.  This
    algorithm ignores any edge weights and is not defined for directed
    graphs or graphs with parallel edges or self loops.
    Estimates for appropriate values of `Q` are found in [2]_.
    References
    ----------
    .. [1] Julian J. McAuley, Luciano da Fontoura Costa,
       and Tibério S. Caetano,
       "The rich-club phenomenon across complex network hierarchies",
       Applied Physics Letters Vol 91 Issue 8, August 2007.
       https://arxiv.org/abs/physics/0701290
    .. [2] R. Milo, N. Kashtan, S. Itzkovitz, M. E. J. Newman, U. Alon,
       "Uniform generation of random graphs with arbitrary degree
       sequences", 2006. https://arxiv.org/abs/cond-mat/0312028
    """
    if nx.number_of_selfloops(G) > 0:
        raise Exception(
            "rich_club_coefficient is not implemented for " "graphs with self loops."
        )
    rc = _compute_rc(G)
    if normalized:
        # make R a copy of G, randomize with Q*|E| double edge swaps
        # and use rich_club coefficient of R to normalize
        R = G.copy()
        E = R.number_of_edges()
        nx.double_edge_swap(R, Q * E, max_tries=Q * E * 10, seed=seed)
        rcran = _compute_rc(R)
        rc = {k: v / rcran[k] for k, v in rc.items()}
    return rc