human
Complete the below
@not_implemented_for('directed')
@not_implemented_for('multigraph')
def adamic_adar_index(G, ebunch=None):

    ''''Compute the Adamic-Adar index of all node pairs in ebunch.
    Adamic-Adar index of `u` and `v` is defined as
    .. math::
    \sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{1}{\log |\Gamma(w)|}
    where :math:`\Gamma(u)` denotes the set of neighbors of `u`.
    Parameters
    G : graph
    NetworkX undirected graph.
    ebunch : iterable of node pairs, optional (default = None)
    Adamic-Adar index will be computed for each pair of nodes given
    in the iterable. The pairs must be given as 2-tuples (u, v)
    where u and v are nodes in the graph. If ebunch is None then all
    non-existent edges in the graph will be used.
    Default value: None.
    Returns
    piter : iterator
    An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
    pair of nodes and p is their Adamic-Adar index.
    Examples
    >>> import networkx as nx
    >>> G = nx.complete_graph(5)
    >>> preds = nx.adamic_adar_index(G, [(0, 1), (2, 3)])
    >>> for u, v, p in preds:
    ...     \'(%d, %d) -> %.8f\' % (u, v, p)
    \'(0, 1) -> 2.16404256\'
    \'(2, 3) -> 2.16404256\'
    References
    .. [1] D. Liben-Nowell, J. Kleinberg.
    The Link Prediction Problem for Social Networks (2004).
    http://www.cs.cornell.edu/home/kleinber/link-pred.pdf'
    '''
assistant
#Here is Python program to compute the Adamic-Adar index of all node pairs in ebunch.
    def predict(u, v):
        return sum(((1 / log(G.degree(w))) for w in nx.common_neighbors(G, u, v)))
    return _apply_prediction(G, predict, ebunch)