__init__.py 44.2 KB
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    or smaller than a given radius.

    See Also
    --------
    triangulation: 2D or 3D triangulation
    random_graph: random graph generation
    lattice : N-dimensional square lattice

    Examples
    --------
    >>> from numpy.random import seed, random
    >>> seed(42)
    >>> points = random((500, 2)) * 4
    >>> g, pos = gt.geometric_graph(points, 0.3)
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    >>> gt.graph_draw(g, pos=pos, pin=True, size=(8,8), output="geometric.pdf")
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    <...>
    >>> g, pos = gt.geometric_graph(points, 0.3, [(0,4), (0,4)])
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    >>> gt.graph_draw(g, size=(8,8), output="geometric_periodic.pdf")
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    <...>

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    .. image:: geometric.*
    .. image:: geometric_periodic.*
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    *Left:* Geometric network with random points. *Right:* Same network, but
     with periodic boundary conditions.

    References
    ----------
    .. [geometric-graph] Jesper Dall and Michael Christensen, "Random geometric
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       graphs", Phys. Rev. E 66, 016121 (2002), :doi:`10.1103/PhysRevE.66.016121`
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    """

    g = Graph(directed=False)
    pos = g.new_vertex_property("vector<double>")
    if type(points) != numpy.ndarray:
        points = numpy.array(points)
    if len(points.shape) < 2:
        raise ValueError("points list must be a two-dimensional array!")
    if ranges is not None:
        periodic = True
        if type(ranges) != numpy.ndarray:
            ranges = numpy.array(ranges, dtype="float")
        else:
            ranges = array(ranges, dtype="float")
    else:
        periodic = False
        ranges = ()

    libgraph_tool_generation.geometric(g._Graph__graph, points, float(radius),
                                       ranges, periodic,
                                       _prop("v", g, pos))
    return g, pos
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def price_network(N, m=1, c=None, gamma=1, directed=True, seed_graph=None):
    r"""A generalized version of Price's -- or Barabási-Albert if undirected -- preferential attachment network model.

    Parameters
    ----------
    N : int
        Size of the network.
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    m : int (optional, default: ``1``)
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        Out-degree of newly added vertices.
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    c : float (optional, default: ``1 if directed == True else 0``)
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        Constant factor added to the probability of a vertex receiving an edge
        (see notes below).
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    gamma : float (optional, default: ``1``)
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        Preferential attachment power (see notes below).
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    directed : bool (optional, default: ``True``)
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        If ``True``, a Price network is generated. If ``False``, a
        Barabási-Albert network is generated.
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    seed_graph : :class:`~graph_tool.Graph` (optional, default: ``None``)
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        If provided, this graph will be used as the starting point of the
        algorithm.

    Returns
    -------
    price_graph : :class:`~graph_tool.Graph`
        The generated graph.

    Notes
    -----

    The (generalized) [price]_ network is either a directed or undirected graph
    (the latter is called a Barabási-Albert network), generated dynamically by
    at each step adding a new vertex, and connecting it to :math:`m` other
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    vertices, chosen with probability :math:`\pi` defined as:
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    .. math::

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        \pi \propto k^\gamma + c
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    where :math:`k` is the in-degree of the vertex (or simply the degree in the
    undirected case). If :math:`\gamma=1`, the tail of resulting in-degree
    distribution of the directed case is given by

    .. math::

        P_{k_\text{in}} \sim k_\text{in}^{-(2 + c/m)},

    or for the undirected case

    .. math::

        P_{k} \sim k^{-(3 + c/m)}.

    However, if :math:`\gamma \ne 1`, the in-degree distribution is not
    scale-free (see [dorogovtsev-evolution]_ for details).

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    Note that if `seed_graph` is not given, the algorithm will *always* start
    with one node if :math:`c > 0`, or with two nodes with a link between them
    otherwise. If :math:`m > 1`, the degree of the newly added vertices will be
    vary dynamically as :math:`m'(t) = \min(m, N(t))`, where :math:`N(t)` is the
    number of vertices added so far. If this behaviour is undesired, a proper
    seed graph with :math:`N \ge m` vertices must be provided.

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    This algorithm runs in :math:`O(N\log N)` time.

    See Also
    --------
    triangulation: 2D or 3D triangulation
    random_graph: random graph generation
    lattice : N-dimensional square lattice
    geometric_graph : N-dimensional geometric network

    Examples
    --------
    >>> from numpy.random import seed, random
    >>> seed(42)
    >>> g = gt.price_network(100000)
    >>> gt.graph_draw(g, layout="sfdp", size=(12,12), vcolor=g.vertex_index,
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    ...               output="price-network.pdf")
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    <...>
    >>> g = gt.price_network(100000, c=0.1)
    >>> gt.graph_draw(g, layout="sfdp", size=(12,12), vcolor=g.vertex_index,
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    ...               output="price-network-broader.pdf")
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    <...>

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    .. image:: price-network.*
    .. image:: price-network-broader.*
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    Price networks with :math:`N=10^5` nodes. **Left:** :math:`c=1`, **Right:**
    :math:`c=0.1`. The colors represent the order in which vertices were
    added.

    References
    ----------

    .. [yule] Yule, G. U. "A Mathematical Theory of Evolution, based on the
       Conclusions of Dr. J. C. Willis, F.R.S.". Philosophical Transactions of
       the Royal Society of London, Ser. B 213: 21–87, 1925,
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       :doi:`10.1098/rstb.1925.0002`
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    .. [price] Derek De Solla Price, "A general theory of bibliometric and other
       cumulative advantage processes", Journal of the American Society for
       Information Science, Volume 27, Issue 5, pages 292–306, September 1976,
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       :doi:`10.1002/asi.4630270505`
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    .. [barabasi-albert] Barabási, A.-L., and Albert, R., "Emergence of
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       scaling in random networks", Science, 286, 509, 1999,
       :doi:`10.1126/science.286.5439.509`
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    .. [dorogovtsev-evolution] S. N. Dorogovtsev and J. F. F. Mendes, "Evolution
       of networks", Advances in Physics, 2002, Vol. 51, No. 4, 1079-1187,
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       :doi:`10.1080/00018730110112519`
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    """

    if c is None:
        c = 1 if directed else 0

    if seed_graph is None:
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        g = Graph(directed=directed)
        if c > 0:
            g.add_vertex()
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        else:
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            g.add_vertex(2)
            g.add_edge(g.vertex(1), g.vertex(0))
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        N -= g.num_vertices()
    else:
        g = seed_graph
    seed = numpy.random.randint(0, sys.maxint)
    libgraph_tool_generation.price(g._Graph__graph, N, gamma, c, m, seed)
    return g