__init__.py 41.1 KB
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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# graph_tool -- a general graph manipulation python module
#
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# Copyright (C) 2006-2017 Tiago de Paula Peixoto <tiago@skewed.de>
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#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

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"""
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``graph_tool.centrality`` - Centrality measures
-----------------------------------------------
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This module includes centrality-related algorithms.
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Summary
+++++++

.. autosummary::
   :nosignatures:

   pagerank
   betweenness
   central_point_dominance
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   closeness
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   eigenvector
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   katz
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   hits
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   eigentrust
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   trust_transitivity
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Contents
++++++++
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"""

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from __future__ import division, absolute_import, print_function

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from .. dl_import import dl_import
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dl_import("from . import libgraph_tool_centrality")
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from .. import _prop, ungroup_vector_property
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from .. topology import shortest_distance
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import sys
import numpy
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import numpy.linalg
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__all__ = ["pagerank", "betweenness", "central_point_dominance", "closeness",
           "eigentrust", "eigenvector", "katz", "hits", "trust_transitivity"]
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def pagerank(g, damping=0.85, pers=None, weight=None, prop=None, epsilon=1e-6,
             max_iter=None, ret_iter=False):
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    r"""
    Calculate the PageRank of each vertex.

    Parameters
    ----------
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    g : :class:`~graph_tool.Graph`
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        Graph to be used.
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    damping : float, optional (default: 0.85)
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        Damping factor.
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    pers : :class:`~graph_tool.PropertyMap`, optional (default: None)
        Personalization vector. If omitted, a constant value of :math:`1/N`
        will be used.
    weight : :class:`~graph_tool.PropertyMap`, optional (default: None)
        Edge weights. If omitted, a constant value of 1 will be used.
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    prop : :class:`~graph_tool.PropertyMap`, optional (default: None)
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        Vertex property map to store the PageRank values. If supplied, it will
        be used uninitialized.
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    epsilon : float, optional (default: 1e-6)
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        Convergence condition. The iteration will stop if the total delta of all
        vertices are below this value.
    max_iter : int, optional (default: None)
        If supplied, this will limit the total number of iterations.
    ret_iter : bool, optional (default: False)
        If true, the total number of iterations is also returned.

    Returns
    -------
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    pagerank : :class:`~graph_tool.PropertyMap`
        A vertex property map containing the PageRank values.
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    See Also
    --------
    betweenness: betweenness centrality
    eigentrust: eigentrust centrality
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    eigenvector: eigenvector centrality
    hits: hubs and authority centralities
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    trust_transitivity: pervasive trust transitivity
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    Notes
    -----
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    The value of PageRank [pagerank-wikipedia]_ of vertex v, :math:`PR(v)`, is
    given iteratively by the relation:
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    .. math::
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        PR(v) = \frac{1-d}{N} + d \sum_{u \in \Gamma^{-}(v)}
                \frac{PR (u)}{d^{+}(u)}
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    where :math:`\Gamma^{-}(v)` are the in-neighbours of v, :math:`d^{+}(w)` is
    the out-degree of w, and d is a damping factor.

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    If a personalization property :math:`p(v)` is given, the definition becomes:

    .. math::

        PR(v) = (1-d)p(v) + d \sum_{u \in \Gamma^{-}(v)}
                \frac{PR (u)}{d^{+}(u)}

    If edge weights are also given, the equation is then generalized to:

    .. math::

        PR(v) = (1-d)p(v) + d \sum_{u \in \Gamma^{-}(v)}
                \frac{PR (u) w_{u\to v}}{d^{+}(u)}

    where :math:`d^{+}(u)=\sum_{y}A_{u,y}w_{u\to y}` is redefined to be the sum
    of the weights of the out-going edges from u.

    The implemented algorithm progressively iterates the above equations, until
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    it no longer changes, according to the parameter epsilon. It has a
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    topology-dependent running time.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: pagerank

       import matplotlib

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    .. doctest:: pagerank

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> pr = gt.pagerank(g)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=pr,
       ...               vertex_size=gt.prop_to_size(pr, mi=5, ma=15),
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       ...               vorder=pr, vcmap=matplotlib.cm.gist_heat,
       ...               output="polblogs_pr.pdf")
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       <...>

    .. testcode:: pagerank
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=pr,
                     vertex_size=gt.prop_to_size(pr, mi=5, ma=15),
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                     vorder=pr, vcmap=matplotlib.cm.gist_heat,
                     output="polblogs_pr.png")
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    .. figure:: polblogs_pr.*
       :align: center

       PageRank values of the a political blogs network of [adamic-polblogs]_.
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    Now with a personalization vector, and edge weights:

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    .. doctest:: pagerank

       >>> d = g.degree_property_map("total")
       >>> periphery = d.a <= 2
       >>> p = g.new_vertex_property("double")
       >>> p.a[periphery] = 100
       >>> pr = gt.pagerank(g, pers=p)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=pr,
       ...               vertex_size=gt.prop_to_size(pr, mi=5, ma=15),
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       ...               vorder=pr, vcmap=matplotlib.cm.gist_heat,
       ...               output="polblogs_pr_pers.pdf")
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       <...>

    .. testcode:: pagerank
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=pr,
                     vertex_size=gt.prop_to_size(pr, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=pr, output="polblogs_pr_pers.png")


    .. figure:: polblogs_pr_pers.*
       :align: center

       Personalized PageRank values of the a political blogs network of
       [adamic-polblogs]_, where vertices with very low degree are given
       artificially high scores.
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    References
    ----------
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    .. [pagerank-wikipedia] http://en.wikipedia.org/wiki/Pagerank
    .. [lawrence-pagerank-1998] P. Lawrence, B. Sergey, M. Rajeev, W. Terry,
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       "The pagerank citation ranking: Bringing order to the web", Technical
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       report, Stanford University, 1998
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    .. [Langville-survey-2005] A. N. Langville, C. D. Meyer, "A Survey of
       Eigenvector Methods for Web Information Retrieval", SIAM Review, vol. 47,
       no. 1, pp. 135-161, 2005, :DOI:`10.1137/S0036144503424786`
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """

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    if max_iter is None:
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        max_iter = 0
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    if prop is None:
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        prop = g.new_vertex_property("double")
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        N = len(prop.fa)
        prop.fa = pers.fa[:N] if pers is not None else 1. / g.num_vertices()
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    ic = libgraph_tool_centrality.\
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            get_pagerank(g._Graph__graph, _prop("v", g, prop),
                         _prop("v", g, pers), _prop("e", g, weight),
                         damping, epsilon, max_iter)
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    if ret_iter:
        return prop, ic
    else:
        return prop

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def betweenness(g, vprop=None, eprop=None, weight=None, norm=True):
    r"""
    Calculate the betweenness centrality for each vertex and edge.

    Parameters
    ----------
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    g : :class:`~graph_tool.Graph`
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        Graph to be used.
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    vprop : :class:`~graph_tool.PropertyMap`, optional (default: None)
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        Vertex property map to store the vertex betweenness values.
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    eprop : :class:`~graph_tool.PropertyMap`, optional (default: None)
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        Edge property map to store the edge betweenness values.
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    weight : :class:`~graph_tool.PropertyMap`, optional (default: None)
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        Edge property map corresponding to the weight value of each edge.
    norm : bool, optional (default: True)
        Whether or not the betweenness values should be normalized.

    Returns
    -------
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    vertex_betweenness : A vertex property map with the vertex betweenness values.
    edge_betweenness : An edge property map with the edge betweenness values.
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    See Also
    --------
    central_point_dominance: central point dominance of the graph
    pagerank: PageRank centrality
    eigentrust: eigentrust centrality
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    eigenvector: eigenvector centrality
    hits: hubs and authority centralities
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    trust_transitivity: pervasive trust transitivity
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    Notes
    -----
    Betweenness centrality of a vertex :math:`C_B(v)` is defined as,

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    .. math::

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        C_B(v)= \sum_{s \neq v \neq t \in V \atop s \neq t}
                \frac{\sigma_{st}(v)}{\sigma_{st}}

    where :math:`\sigma_{st}` is the number of shortest geodesic paths from s to
    t, and :math:`\sigma_{st}(v)` is the number of shortest geodesic paths from
    s to t that pass through a vertex v.  This may be normalised by dividing
    through the number of pairs of vertices not including v, which is
    :math:`(n-1)(n-2)/2`.

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    The algorithm used here is defined in [brandes-faster-2001]_, and has a
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    complexity of :math:`O(VE)` for unweighted graphs and :math:`O(VE + V(V+E)
    \log V)` for weighted graphs. The space complexity is :math:`O(VE)`.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: betweenness

       import matplotlib

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    .. doctest:: betweenness

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> vp, ep = gt.betweenness(g)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=vp,
       ...               vertex_size=gt.prop_to_size(vp, mi=5, ma=15),
       ...               edge_pen_width=gt.prop_to_size(ep, mi=0.5, ma=5),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=vp, output="polblogs_betweenness.pdf")
       <...>

    .. testcode:: betweenness
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=vp,
                     vertex_size=gt.prop_to_size(vp, mi=5, ma=15),
                     edge_pen_width=gt.prop_to_size(ep, mi=0.5, ma=5),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=vp, output="polblogs_betweenness.png")


    .. figure:: polblogs_betweenness.*
       :align: center

       Betweenness values of the a political blogs network of [adamic-polblogs]_.
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    References
    ----------
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    .. [betweenness-wikipedia] http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality
    .. [brandes-faster-2001] U. Brandes, "A faster algorithm for betweenness
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       centrality", Journal of Mathematical Sociology, 2001, :doi:`10.1080/0022250X.2001.9990249`
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """
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    if vprop is None:
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        vprop = g.new_vertex_property("double")
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    if eprop is None:
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        eprop = g.new_edge_property("double")
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    if weight is not None and weight.value_type() != eprop.value_type():
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        nw = g.new_edge_property(eprop.value_type())
        g.copy_property(weight, nw)
        weight = nw
    libgraph_tool_centrality.\
            get_betweenness(g._Graph__graph, _prop("e", g, weight),
                            _prop("e", g, eprop), _prop("v", g, vprop), norm)
    return vprop, eprop

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def closeness(g, weight=None, source=None, vprop=None, norm=True, harmonic=False):
    r"""
    Calculate the closeness centrality for each vertex.

    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    weight : :class:`~graph_tool.PropertyMap`, optional (default: None)
        Edge property map corresponding to the weight value of each edge.
    source : :class:`~graph_tool.Vertex`, optional (default: ``None``)
        If specified, the centrality is computed for this vertex alone.
    vprop : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
        Vertex property map to store the vertex centrality values.
    norm : bool, optional (default: ``True``)
        Whether or not the centrality values should be normalized.
    harmonic : bool, optional (default: ``False``)
        If true, the sum of the inverse of the distances will be computed,
        instead of the inverse of the sum.

    Returns
    -------
    vertex_closeness : :class:`~graph_tool.PropertyMap`
        A vertex property map with the vertex closeness values.

    See Also
    --------
    central_point_dominance: central point dominance of the graph
    pagerank: PageRank centrality
    eigentrust: eigentrust centrality
    eigenvector: eigenvector centrality
    hits: hubs and authority centralities
    trust_transitivity: pervasive trust transitivity

    Notes
    -----
    The closeness centrality of a vertex :math:`i` is defined as,

    .. math::

        c_i = \frac{1}{\sum_j d_{ij}}

    where :math:`d_{ij}` is the (possibly directed and/or weighted) distance
    from :math:`i` to :math:`j`. In case there is no path between the two
    vertices, here the distance is taken to be zero.

    If ``harmonic == True``, the definition becomes

    .. math::

        c_i = \sum_j\frac{1}{d_{ij}},

    but now, in case there is no path between the two vertices, we take
    :math:`d_{ij} \to\infty` such that :math:`1/d_{ij}=0`.

    If ``norm == True``, the values of :math:`c_i` are normalized by
    :math:`n_i-1` where :math:`n_i` is the size of the (out-) component of
    :math:`i`. If ``harmonic == True``, they are instead simply normalized by
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    :math:`V-1`.
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    The algorithm complexity of :math:`O(V(V + E))` for unweighted graphs and
    :math:`O(V(v+E) \log V)` for weighted graphs. If the option ``source`` is
    specified, this drops to :math:`O(V + E)` and :math:`O((V+E)\log V)`
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    respectively.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------

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    .. testsetup:: closeness

       import matplotlib

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    .. doctest:: closeness

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> c = gt.closeness(g)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=c,
       ...               vertex_size=gt.prop_to_size(c, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=c, output="polblogs_closeness.pdf")
       <...>

    .. testcode:: closeness
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=c,
                     vertex_size=gt.prop_to_size(c, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=c, output="polblogs_closeness.png")


    .. figure:: polblogs_closeness.*
       :align: center

       Closeness values of the a political blogs network of [adamic-polblogs]_.

    References
    ----------
    .. [closeness-wikipedia] https://en.wikipedia.org/wiki/Closeness_centrality
    .. [opsahl-node-2010] Opsahl, T., Agneessens, F., Skvoretz, J., "Node
       centrality in weighted networks: Generalizing degree and shortest
       paths". Social Networks 32, 245-251, 2010 :DOI:`10.1016/j.socnet.2010.03.006`
    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`

    """
    if source is None:
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        if vprop is None:
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            vprop = g.new_vertex_property("double")
        libgraph_tool_centrality.\
            closeness(g._Graph__graph, _prop("e", g, weight),
                      _prop("v", g, vprop), harmonic, norm)
        return vprop
    else:
        max_dist = g.num_vertices() + 1
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        dist = shortest_distance(g, source=source, weights=weight,
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                                 max_dist=max_dist)
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        dists = dist.fa[(dist.fa < max_dist) * (dist.fa > 0)]
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        if harmonic:
            c = (1. / dists).sum()
            if norm:
                c /= g.num_vertices() - 1
        else:
            c = 1. / dists.sum()
            if norm:
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                c *= len(dists)
        return c
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def central_point_dominance(g, betweenness):
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    r"""
    Calculate the central point dominance of the graph, given the betweenness
    centrality of each vertex.

    Parameters
    ----------
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    g : :class:`~graph_tool.Graph`
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        Graph to be used.
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    betweenness : :class:`~graph_tool.PropertyMap`
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        Vertex property map with the betweenness centrality values. The values
        must be normalized.

    Returns
    -------
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    cp : float
        The central point dominance.
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    See Also
    --------
    betweenness: betweenness centrality

    Notes
    -----
    Let :math:`v^*` be the vertex with the largest relative betweenness
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    centrality; then, the central point dominance [freeman-set-1977]_ is defined
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    as:

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    .. math::

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        C'_B = \frac{1}{|V|-1} \sum_{v} C_B(v^*) - C_B(v)

    where :math:`C_B(v)` is the normalized betweenness centrality of vertex
    v. The value of :math:`C_B` lies in the range [0,1].

    The algorithm has a complexity of :math:`O(V)`.

    Examples
    --------
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    >>> g = gt.collection.data["polblogs"]
    >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
    >>> vp, ep = gt.betweenness(g)
    >>> print(gt.central_point_dominance(g, vp))
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    0.11610685614...
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    References
    ----------
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    .. [freeman-set-1977] Linton C. Freeman, "A Set of Measures of Centrality
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       Based on Betweenness", Sociometry, Vol. 40, No. 1,  pp. 35-41, 1977,
       `http://www.jstor.org/stable/3033543 <http://www.jstor.org/stable/3033543>`_
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    """

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    return libgraph_tool_centrality.\
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           get_central_point_dominance(g._Graph__graph,
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                                       _prop("v", g, betweenness))

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def eigenvector(g, weight=None, vprop=None, epsilon=1e-6, max_iter=None):
    r"""
    Calculate the eigenvector centrality of each vertex in the graph, as well as
    the largest eigenvalue.

    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
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    weight : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        Edge property map with the edge weights.
    vprop : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
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        Vertex property map where the values of eigenvector must be stored. If
        provided, it will be used uninitialized.
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    epsilon : float, optional (default: ``1e-6``)
        Convergence condition. The iteration will stop if the total delta of all
        vertices are below this value.
    max_iter : int, optional (default: ``None``)
        If supplied, this will limit the total number of iterations.

    Returns
    -------
    eigenvalue : float
        The largest eigenvalue of the (weighted) adjacency matrix.
    eigenvector : :class:`~graph_tool.PropertyMap`
        A vertex property map containing the eigenvector values.

    See Also
    --------
    betweenness: betweenness centrality
    pagerank: PageRank centrality
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    hits: hubs and authority centralities
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    trust_transitivity: pervasive trust transitivity

    Notes
    -----

    The eigenvector centrality :math:`\mathbf{x}` is the eigenvector of the
    (weighted) adjacency matrix with the largest eigenvalue :math:`\lambda`,
    i.e. it is the solution of

    .. math::

        \mathbf{A}\mathbf{x} = \lambda\mathbf{x},


    where :math:`\mathbf{A}` is the (weighted) adjacency matrix and
    :math:`\lambda` is the largest eigenvalue.

    The algorithm uses the power method which has a topology-dependent complexity of
    :math:`O\left(N\times\frac{-\log\epsilon}{\log|\lambda_1/\lambda_2|}\right)`,
    where :math:`N` is the number of vertices, :math:`\epsilon` is the ``epsilon``
    parameter, and :math:`\lambda_1` and :math:`\lambda_2` are the largest and
    second largest eigenvalues of the (weighted) adjacency matrix, respectively.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: eigenvector

       np.random.seed(42)
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       import matplotlib
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    .. doctest:: eigenvector

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> w = g.new_edge_property("double")
       >>> w.a = np.random.random(len(w.a)) * 42
       >>> ee, x = gt.eigenvector(g, w)
       >>> print(ee)
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       724.302745922...
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       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
       ...               vertex_size=gt.prop_to_size(x, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=x, output="polblogs_eigenvector.pdf")
       <...>

    .. testcode:: eigenvector
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
                     vertex_size=gt.prop_to_size(x, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=x, output="polblogs_eigenvector.png")


    .. figure:: polblogs_eigenvector.*
       :align: center

       Eigenvector values of the a political blogs network of
       [adamic-polblogs]_, with random weights attributed to the edges.
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    References
    ----------

    .. [eigenvector-centrality] http://en.wikipedia.org/wiki/Centrality#Eigenvector_centrality
    .. [power-method] http://en.wikipedia.org/wiki/Power_iteration
    .. [langville-survey-2005] A. N. Langville, C. D. Meyer, "A Survey of
       Eigenvector Methods for Web Information Retrieval", SIAM Review, vol. 47,
       no. 1, pp. 135-161, 2005, :DOI:`10.1137/S0036144503424786`
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """

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    if vprop is None:
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        vprop = g.new_vertex_property("double")
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        vprop.fa = 1. / g.num_vertices()
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    if max_iter is None:
        max_iter = 0
    ee = libgraph_tool_centrality.\
         get_eigenvector(g._Graph__graph, _prop("e", g, weight),
                         _prop("v", g, vprop), epsilon, max_iter)
    return ee, vprop


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def katz(g, alpha=0.01, beta=None, weight=None, vprop=None, epsilon=1e-6,
         max_iter=None, norm=True):
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    r"""
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    Calculate the Katz centrality of each vertex in the graph.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    weight : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
        Edge property map with the edge weights.
    alpha : float, optional (default: ``0.01``)
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        Free parameter :math:`\alpha`. This must be smaller than the inverse of
        the largest eigenvalue of the adjacency matrix.
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    beta : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
        Vertex property map where the local personalization values. If not
        provided, the global value of 1 will be used.
    vprop : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
        Vertex property map where the values of eigenvector must be stored. If
        provided, it will be used uninitialized.
    epsilon : float, optional (default: ``1e-6``)
        Convergence condition. The iteration will stop if the total delta of all
        vertices are below this value.
    max_iter : int, optional (default: ``None``)
        If supplied, this will limit the total number of iterations.
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    norm : bool, optional (default: ``True``)
        Whether or not the centrality values should be normalized.
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    Returns
    -------
    centrality : :class:`~graph_tool.PropertyMap`
        A vertex property map containing the Katz centrality values.

    See Also
    --------
    betweenness: betweenness centrality
    pagerank: PageRank centrality
    eigenvector: eigenvector centrality
    hits: hubs and authority centralities
    trust_transitivity: pervasive trust transitivity

    Notes
    -----

    The Katz centrality :math:`\mathbf{x}` is the solution of the nonhomogeneous
    linear system

    .. math::

        \mathbf{x} = \alpha\mathbf{A}\mathbf{x} + \mathbf{\beta},


    where :math:`\mathbf{A}` is the (weighted) adjacency matrix and
    :math:`\mathbf{\beta}` is the personalization vector (if not supplied,
    :math:`\mathbf{\beta} = \mathbf{1}` is assumed).

    The algorithm uses successive iterations of the equation above, which has a
    topology-dependent convergence complexity.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: katz

       np.random.seed(42)
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       import matplotlib
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    .. doctest:: katz

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> w = g.new_edge_property("double")
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       >>> w.a = np.random.random(len(w.a))
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       >>> x = gt.katz(g, weight=w)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
       ...               vertex_size=gt.prop_to_size(x, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=x, output="polblogs_katz.pdf")
       <...>

    .. testcode:: katz
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
                     vertex_size=gt.prop_to_size(x, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=x, output="polblogs_katz.png")


    .. figure:: polblogs_katz.*
       :align: center

       Katz centrality values of the a political blogs network of
       [adamic-polblogs]_, with random weights attributed to the edges.
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    References
    ----------

    .. [katz-centrality] http://en.wikipedia.org/wiki/Katz_centrality
    .. [katz-new] L. Katz, "A new status index derived from sociometric analysis",
       Psychometrika 18, Number 1, 39-43, 1953, :DOI:`10.1007/BF02289026`
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """

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    if vprop is None:
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        vprop = g.new_vertex_property("double")
    if max_iter is None:
        max_iter = 0
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    libgraph_tool_centrality.\
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         get_katz(g._Graph__graph, _prop("e", g, weight), _prop("v", g, vprop),
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                  _prop("v", g, beta), float(alpha), epsilon, max_iter)
    if norm:
        vprop.fa = vprop.fa / numpy.linalg.norm(vprop.fa)
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    return vprop


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def hits(g, weight=None, xprop=None, yprop=None, epsilon=1e-6, max_iter=None):
    r"""
    Calculate the authority and hub centralities of each vertex in the graph.

    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    weight : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
        Edge property map with the edge weights.
    xprop : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
        Vertex property map where the authority centrality must be stored.
    yprop : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
        Vertex property map where the hub centrality must be stored.
    epsilon : float, optional (default: ``1e-6``)
        Convergence condition. The iteration will stop if the total delta of all
        vertices are below this value.
    max_iter : int, optional (default: ``None``)
        If supplied, this will limit the total number of iterations.

    Returns
    -------
    eig : `float`
        The largest eigenvalue of the cocitation matrix.
    x : :class:`~graph_tool.PropertyMap`
        A vertex property map containing the authority centrality values.
    y : :class:`~graph_tool.PropertyMap`
        A vertex property map containing the hub centrality values.

    See Also
    --------
    betweenness: betweenness centrality
    eigenvector: eigenvector centrality
    pagerank: PageRank centrality
    trust_transitivity: pervasive trust transitivity

    Notes
    -----

    The Hyperlink-Induced Topic Search (HITS) centrality assigns hub
    (:math:`\mathbf{y}`) and authority (:math:`\mathbf{x}`) centralities to the
    vertices, following:

    .. math::

        \begin{align}
            \mathbf{x} &= \alpha\mathbf{A}\mathbf{y} \\
            \mathbf{y} &= \beta\mathbf{A}^T\mathbf{x}
        \end{align}


    where :math:`\mathbf{A}` is the (weighted) adjacency matrix and
    :math:`\lambda = 1/(\alpha\beta)` is the largest eigenvalue of the
    cocitation matrix, :math:`\mathbf{A}\mathbf{A}^T`. (Without loss of
    generality, we set :math:`\beta=1` in the algorithm.)

    The algorithm uses the power method which has a topology-dependent complexity of
    :math:`O\left(N\times\frac{-\log\epsilon}{\log|\lambda_1/\lambda_2|}\right)`,
    where :math:`N` is the number of vertices, :math:`\epsilon` is the ``epsilon``
    parameter, and :math:`\lambda_1` and :math:`\lambda_2` are the largest and
    second largest eigenvalues of the (weighted) cocitation matrix, respectively.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: hits

       import matplotlib

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    .. doctest:: hits

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> ee, x, y = gt.hits(g)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
       ...               vertex_size=gt.prop_to_size(x, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=x, output="polblogs_hits_auths.pdf")
       <...>
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=y,
       ...               vertex_size=gt.prop_to_size(y, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=y, output="polblogs_hits_hubs.pdf")
       <...>

    .. testcode:: hits
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
                     vertex_size=gt.prop_to_size(x, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=x, output="polblogs_hits_auths.png")
       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=y,
                     vertex_size=gt.prop_to_size(y, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=y, output="polblogs_hits_hubs.png")


    .. figure:: polblogs_hits_auths.*
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       :align: center
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       HITS authority values of the a political blogs network of
       [adamic-polblogs]_.

    .. figure:: polblogs_hits_hubs.*
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       :align: center
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       HITS hub values of the a political blogs network of [adamic-polblogs]_.
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    References
    ----------

    .. [hits-algorithm] http://en.wikipedia.org/wiki/HITS_algorithm
    .. [kleinberg-authoritative] J. Kleinberg, "Authoritative sources in a
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       hyperlinked environment", Journal of the ACM 46 (5): 604-632, 1999,
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       :DOI:`10.1145/324133.324140`.
    .. [power-method] http://en.wikipedia.org/wiki/Power_iteration
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """

    if xprop is None:
        xprop = g.new_vertex_property("double")
    if yprop is None:
        yprop = g.new_vertex_property("double")
    if max_iter is None:
        max_iter = 0
    l = libgraph_tool_centrality.\
         get_hits(g._Graph__graph, _prop("e", g, weight), _prop("v", g, xprop),
                  _prop("v", g, yprop), epsilon, max_iter)
    return 1. / l, xprop, yprop


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def eigentrust(g, trust_map, vprop=None, norm=False, epsilon=1e-6, max_iter=0,
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               ret_iter=False):
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    r"""
    Calculate the eigentrust centrality of each vertex in the graph.

    Parameters
    ----------
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    g : :class:`~graph_tool.Graph`
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        Graph to be used.
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    trust_map : :class:`~graph_tool.PropertyMap`
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        Edge property map with the values of trust associated with each
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        edge. The values must lie in the range [0,1].
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    vprop : :class:`~graph_tool.PropertyMap`, optional (default: ``None``)
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        Vertex property map where the values of eigentrust must be stored.
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    norm : bool, optional (default:  ``False``)
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        Norm eigentrust values so that the total sum equals 1.
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    epsilon : float, optional (default: ``1e-6``)
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        Convergence condition. The iteration will stop if the total delta of all
        vertices are below this value.
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    max_iter : int, optional (default: ``None``)
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        If supplied, this will limit the total number of iterations.
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    ret_iter : bool, optional (default: ``False``)
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        If true, the total number of iterations is also returned.

    Returns
    -------
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    eigentrust : :class:`~graph_tool.PropertyMap`
        A vertex property map containing the eigentrust values.
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    See Also
    --------
    betweenness: betweenness centrality
    pagerank: PageRank centrality
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    trust_transitivity: pervasive trust transitivity
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    Notes
    -----
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    The eigentrust [kamvar-eigentrust-2003]_ values :math:`t_i` correspond the
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    following limit

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    .. math::

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        \mathbf{t} = \lim_{n\to\infty} \left(C^T\right)^n \mathbf{c}

    where :math:`c_i = 1/|V|` and the elements of the matrix :math:`C` are the
    normalized trust values:

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    .. math::

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        c_{ij} = \frac{\max(s_{ij},0)}{\sum_{j} \max(s_{ij}, 0)}

    The algorithm has a topology-dependent complexity.

    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: eigentrust

       np.random.seed(42)
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       import matplotlib
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    .. doctest:: eigentrust

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> w = g.new_edge_property("double")
       >>> w.a = np.random.random(len(w.a)) * 42
       >>> t = gt.eigentrust(g, w)
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=t,
       ...               vertex_size=gt.prop_to_size(t, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=t, output="polblogs_eigentrust.pdf")
       <...>

    .. testcode:: eigentrust
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=t,
                     vertex_size=gt.prop_to_size(t, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=t, output="polblogs_eigentrust.png")


    .. figure:: polblogs_eigentrust.*
       :align: center

       Eigentrust values of the a political blogs network of
       [adamic-polblogs]_, with random weights attributed to the edges.

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    References
    ----------
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    .. [kamvar-eigentrust-2003] S. D. Kamvar, M. T. Schlosser, H. Garcia-Molina
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       "The eigentrust algorithm for reputation management in p2p networks",
       Proceedings of the 12th international conference on World Wide Web,
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       Pages: 640 - 651, 2003, :doi:`10.1145/775152.775242`
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """

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    if vprop is None:
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        vprop = g.new_vertex_property("double")
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    i = libgraph_tool_centrality.\
           get_eigentrust(g._Graph__graph, _prop("e", g, trust_map),
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                          _prop("v", g, vprop), epsilon, max_iter)
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    if norm:
        vprop.get_array()[:] /= sum(vprop.get_array())

    if ret_iter:
        return vprop, i
    else:
        return vprop

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def trust_transitivity(g, trust_map, source=None, target=None, vprop=None):
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    r"""
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    Calculate the pervasive trust transitivity between chosen (or all) vertices
    in the graph.
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    Parameters
    ----------
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    g : :class:`~graph_tool.Graph`
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        Graph to be used.
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    trust_map : :class:`~graph_tool.PropertyMap`
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        Edge property map with the values of trust associated with each
        edge. The values must lie in the range [0,1].
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    source : :class:`~graph_tool.Vertex` (optional, default: None)
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        Source vertex. All trust values are computed relative to this vertex.
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        If left unspecified, the trust values for all sources are computed.
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    target : :class:`~graph_tool.Vertex` (optional, default: None)
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        The only target for which the trust value will be calculated. If left
        unspecified, the trust values for all targets are computed.
    vprop : :class:`~graph_tool.PropertyMap` (optional, default: None)
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        A vertex property map where the values of transitive trust must be
        stored.
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    Returns
    -------
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    trust_transitivity : :class:`~graph_tool.PropertyMap` or float
        A vertex vector property map containing, for each source vertex, a
        vector with the trust values for the other vertices. If only one of
        `source` or `target` is specified, this will be a single-valued vertex
        property map containing the trust vector from/to the source/target
        vertex to/from the rest of the network. If both `source` and `target`
        are specified, the result is a single float, with the corresponding
        trust value for the target.
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    See Also
    --------
    eigentrust: eigentrust centrality
    betweenness: betweenness centrality
    pagerank: PageRank centrality

    Notes
    -----
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    The pervasive trust transitivity between vertices i and j is defined as
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    .. math::

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        t_{ij} = \frac{\sum_m A_{m,j} w^2_{G\setminus\{j\}}(i\to m)c_{m,j}}
                 {\sum_m A_{m,j} w_{G\setminus\{j\}}(i\to m)}
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    where :math:`A_{ij}` is the adjacency matrix, :math:`c_{ij}` is the direct
    trust from i to j, and :math:`w_G(i\to j)` is the weight of the path with
    maximum weight from i to j, computed as
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    .. math::

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       w_G(i\to j) = \prod_{e\in i\to j} c_e.
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    The algorithm measures the transitive trust by finding the paths with
    maximum weight, using Dijkstra's algorithm, to all in-neighbours of a given
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    target. This search needs to be performed repeatedly for every target, since
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    it needs to be removed from the graph first. For each given source, the
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    resulting complexity is therefore :math:`O(V^2\log V)` for all targets, and
    :math:`O(V\log V)` for a single target. For a given target, the complexity
    for obtaining the trust from all given sources is :math:`O(kV\log V)`, where
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    :math:`k` is the in-degree of the target. Thus, the complexity for obtaining
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    the complete trust matrix is :math:`O(EV\log V)`, where :math:`E` is the
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    number of edges in the network.
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    If enabled during compilation, this algorithm runs in parallel.

    Examples
    --------
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    .. testsetup:: trust_transitivity

       np.random.seed(42)
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       import matplotlib
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    .. doctest:: trust_transitivity

       >>> g = gt.collection.data["polblogs"]
       >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
       >>> g = gt.Graph(g, prune=True)
       >>> w = g.new_edge_property("double")
       >>> w.a = np.random.random(len(w.a))
       >>> g.vp["label"][g.vertex(42)]
       'blogforamerica.com'
       >>> t = gt.trust_transitivity(g, w, source=g.vertex(42))
       >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=t,
       ...               vertex_size=gt.prop_to_size(t, mi=5, ma=15),
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       ...               vcmap=matplotlib.cm.gist_heat,
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       ...               vorder=t, output="polblogs_trust_transitivity.pdf")
       <...>

    .. testcode:: trust_transitivity
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=t,
                     vertex_size=gt.prop_to_size(t, mi=5, ma=15),
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                     vcmap=matplotlib.cm.gist_heat,
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                     vorder=t, output="polblogs_trust_transitivity.png")


    .. figure:: polblogs_trust_transitivity.*
       :align: center

       Trust transitivity values from source vertex 42 of the a political blogs
       network of [adamic-polblogs]_, with random weights attributed to the
       edges.
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    References
    ----------
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    .. [richters-trust-2010] Oliver Richters and Tiago P. Peixoto, "Trust
       Transitivity in Social Networks," PLoS ONE 6, no. 4:
       e1838 (2011), :doi:`10.1371/journal.pone.0018384`
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    .. [adamic-polblogs] L. A. Adamic and N. Glance, "The political blogosphere
       and the 2004 US Election", in Proceedings of the WWW-2005 Workshop on the
       Weblogging Ecosystem (2005). :DOI:`10.1145/1134271.1134277`
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    """
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    if vprop is None:
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        vprop = g.new_vertex_property("vector<double>")
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    if target is None:
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        target = -1
    else:
        target = g.vertex_index[target]
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    if source is None:
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        source = -1
    else:
        source = g.vertex_index[source]

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    libgraph_tool_centrality.\
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            get_trust_transitivity(g._Graph__graph, source, target,
                                   _prop("e", g, trust_map),
                                   _prop("v", g, vprop))
    if target != -1 or source != -1:
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        vprop = ungroup_vector_property(vprop, [0])[0]
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    if target != -1 and source != -1:
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        return vprop.a[target]
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    return vprop