Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/networkx/algorithms/vitality.py

@@ -0,0 +1,75 @@
+"""
+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}