comparison env/lib/python3.9/site-packages/networkx/algorithms/vitality.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

0:4f3585e2f14b

"""
Vitality measures.
"""
from functools import partial

import networkx as nx

__all__ = ["closeness_vitality"]


def closeness_vitality(G, node=None, weight=None, wiener_index=None):
    """Returns the closeness vitality for nodes in the graph.

    The *closeness vitality* of a node, defined in Section 3.6.2 of [1],
    is the change in the sum of distances between all node pairs when
    excluding that node.

    Parameters
    ----------
    G : NetworkX graph
        A strongly-connected graph.

    weight : string
        The name of the edge attribute used as weight. This is passed
        directly to the :func:`~networkx.wiener_index` function.

    node : object
        If specified, only the closeness vitality for this node will be
        returned. Otherwise, a dictionary mapping each node to its
        closeness vitality will be returned.

    Other parameters
    ----------------
    wiener_index : number
        If you have already computed the Wiener index of the graph
        `G`, you can provide that value here. Otherwise, it will be
        computed for you.

    Returns
    -------
    dictionary or float
        If `node` is None, this function returns a dictionary
        with nodes as keys and closeness vitality as the
        value. Otherwise, it returns only the closeness vitality for the
        specified `node`.

        The closeness vitality of a node may be negative infinity if
        removing that node would disconnect the graph.

    Examples
    --------
    >>> G = nx.cycle_graph(3)
    >>> nx.closeness_vitality(G)
    {0: 2.0, 1: 2.0, 2: 2.0}

    See Also
    --------
    closeness_centrality

    References
    ----------
    .. [1] Ulrik Brandes, Thomas Erlebach (eds.).
           *Network Analysis: Methodological Foundations*.
           Springer, 2005.
           <http://books.google.com/books?id=TTNhSm7HYrIC>

    """
    if wiener_index is None:
        wiener_index = nx.wiener_index(G, weight=weight)
    if node is not None:
        after = nx.wiener_index(G.subgraph(set(G) - {node}), weight=weight)
        return wiener_index - after
    vitality = partial(closeness_vitality, G, weight=weight, wiener_index=wiener_index)
    # TODO This can be trivially parallelized.
    return {v: vitality(node=v) for v in G}