def hits_scipy(G,max_iter=100,tol=1.0e-6,normalized=True):
    """Return HITS hubs and authorities values for nodes.

    The HITS algorithm computes two numbers for a node.
    Authorities estimates the node value based on the incoming links.
    Hubs estimates the node value based on outgoing links.

    Parameters
    ----------
    G : graph
      A NetworkX graph

    max_iter : interger, optional
      Maximum number of iterations in power method.

    tol : float, optional
      Error tolerance used to check convergence in power method iteration.

    nstart : dictionary, optional
      Starting value of each node for power method iteration.

    normalized : bool (default=True)
       Normalize results by the sum of all of the values.

    Returns
    -------
    (hubs,authorities) : two-tuple of dictionaries
       Two dictionaries keyed by node containing the hub and authority
       values.

    Examples
    --------
    >>> G=nx.path_graph(4)
    >>> h,a=nx.hits(G)

    Notes
    -----
    This implementation uses SciPy sparse matrices.

    The eigenvector calculation is done by the power iteration method
    and has no guarantee of convergence.  The iteration will stop
    after max_iter iterations or an error tolerance of
    number_of_nodes(G)*tol has been reached.

    The HITS algorithm was designed for directed graphs but this
    algorithm does not check if the input graph is directed and will
    execute on undirected graphs.

    References
    ----------
    .. [1] A. Langville and C. Meyer,
       "A survey of eigenvector methods of web information retrieval."
       http://citeseer.ist.psu.edu/713792.html
    .. [2] Jon Kleinberg,
       Authoritative sources in a hyperlinked environment
       Journal of the ACM 46 (5): 604-632, 1999.
       doi:10.1145/324133.324140.
       http://www.cs.cornell.edu/home/kleinber/auth.pdf.
    """
    try:
        import scipy.sparse
        import numpy as np
    except __HOLE__:
        raise ImportError(\
            "hits_scipy() requires SciPy: http://scipy.org/")
    if len(G) == 0:
        return {},{}
    M = nx.to_scipy_sparse_matrix(G, nodelist=list(G))
    (n,m)=M.shape # should be square
    A=M.T*M # authority matrix
    x=scipy.ones((n,1))/n  # initial guess
    # power iteration on authority matrix
    i=0
    while True:
        xlast=x
        x=A*x
        x=x/x.max()
        # check convergence, l1 norm
        err=scipy.absolute(x-xlast).sum()
        if err < tol:
            break
        if i>max_iter:
            raise NetworkXError(\
            "HITS: power iteration failed to converge in %d iterations."%(i+1))
        i+=1

    a=np.asarray(x).flatten()
    # h=M*a
    h=np.asarray(M*a).flatten()
    if normalized:
        h = h/h.sum()
        a = a/a.sum()
    hubs = dict(zip(G, map(float, h)))
    authorities = dict(zip(G, map(float, a)))
    return hubs,authorities

# fixture for nose tests
dataset/ETHPy150Open networkx/networkx/networkx/algorithms/link_analysis/hits_alg.py/hits_scipy