human
Can you provide a Python program to calculate dispersion between `u` and `v` in `G` given the following:
Complete the below
def dispersion(G, u=None, v=None, normalized=True, alpha=1.0, b=0.0, c=0.0):

    ''''Calculate dispersion between `u` and `v` in `G`.
    A link between two actors (`u` and `v`) has a high dispersion when their
    mutual ties (`s` and `t`) are not well connected with each other.
    Parameters
    G : graph
    A NetworkX graph.
    u : node, optional
    The source for the dispersion score (e.g. ego node of the network).
    v : node, optional
    The target of the dispersion score if specified.
    normalized : bool
    If True (default) normalize by the embededness of the nodes (u and v).
    Returns
    nodes : dictionary
    If u (v) is specified, returns a dictionary of nodes with dispersion
    score for all "target" ("source") nodes. If neither u nor v is
    specified, returns a dictionary of dictionaries for all nodes \'u\' in the
    graph with a dispersion score for each node \'v\'.
    Notes
    This implementation follows Lars Backstrom and Jon Kleinberg [1]_. Typical
    usage would be to run dispersion on the ego network :math:`G_u` if `u` were
    specified.  Running :func:`dispersion` with neither `u` nor `v` specified
    can take some time to complete.
    References
    .. [1] Romantic Partnerships and the Dispersion of Social Ties:
    A Network Analysis of Relationship Status on Facebook.
    Lars Backstrom, Jon Kleinberg.
    http://arxiv.org/pdf/1310.6753v1.pdf'
    '''
assistant
#Here is Python program to calculate dispersion between `u` and `v` in `G`.
    def _dispersion(G_u, u, v):
        "dispersion    for    all    nodes    'v'    in    a    ego    network    G_u    of    node    'u'"
        u_nbrs = set(G_u[u])
        ST = set((n for n in G_u[v] if (n in u_nbrs)))
        set_uv = set([u, v])
        possib = combinations(ST, 2)
        total = 0
        for (s, t) in possib:
            nbrs_s = (u_nbrs.intersection(G_u[s]) - set_uv)
            if (not (t in nbrs_s)):
                if nbrs_s.isdisjoint(G_u[t]):
                    total += 1
        embededness = len(ST)
        if normalized:
            if ((embededness + c) != 0):
                norm_disp = (((total + b) ** alpha) / (embededness + c))
            else:
                norm_disp = ((total + b) ** alpha)
            dispersion = norm_disp
        else:
            dispersion = total
        return dispersion
    if (u is None):
        if (v is None):
            results = dict(((n, {}) for n in G))
            for u in G:
                for v in G[u]:
                    results[u][v] = _dispersion(G, u, v)
        else:
            results = dict.fromkeys(G[v], {})
            for u in G[v]:
                results[u] = _dispersion(G, v, u)
    elif (v is None):
        results = dict.fromkeys(G[u], {})
        for v in G[u]:
            results[v] = _dispersion(G, u, v)
    else:
        results = _dispersion(G, u, v)
    return results