def generate_lfr_params(num_nodes, mixing_param):
    """Generates a dictionary of LFR benchmark parameters for a given network size and mixing parameter.

    Parameters
    ----------
    num_nodes : int
        Number of nodes in the graph.
    mixing_param : float
        Global mixing parameter for the graph.

    Returns
    ------
    dict
        A dictionary of LFR benchmark parameters.
    """

    # generic LFR parameters
    max_community = int(0.2 * num_nodes)
    min_community = int(max_community * 0.25)
    max_degree = int(max_community * 0.3)
    min_degree = int(min_community * 0.4)
    gamma = 3.5  # Power law exponent for the degree distribution
    beta = 1.1  # Power law exponent for the community size distribution

    params = {'n': num_nodes, 'tau1': gamma, 'tau2': beta, 'mu': mixing_param, 'min_degree': min_degree,
              'max_degree': max_degree, 'max_community': max_community, 'min_community': min_community}
    return params