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

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/algorithms/centrality/harmonic.py	Mon Mar 22 18:12:50 2021 +0000
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+"""Functions for computing the harmonic centrality of a graph."""
+from functools import partial
+
+import networkx as nx
+
+__all__ = ["harmonic_centrality"]
+
+
+def harmonic_centrality(G, nbunch=None, distance=None):
+    r"""Compute harmonic centrality for nodes.
+
+    Harmonic centrality [1]_ of a node `u` is the sum of the reciprocal
+    of the shortest path distances from all other nodes to `u`
+
+    .. math::
+
+        C(u) = \sum_{v \neq u} \frac{1}{d(v, u)}
+
+    where `d(v, u)` is the shortest-path distance between `v` and `u`.
+
+    Notice that higher values indicate higher centrality.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    nbunch : container
+      Container of nodes. If provided harmonic centrality will be computed
+      only over the nodes in nbunch.
+
+    distance : edge attribute key, optional (default=None)
+      Use the specified edge attribute as the edge distance in shortest
+      path calculations.  If `None`, then each edge will have distance equal to 1.
+
+    Returns
+    -------
+    nodes : dictionary
+      Dictionary of nodes with harmonic centrality as the value.
+
+    See Also
+    --------
+    betweenness_centrality, load_centrality, eigenvector_centrality,
+    degree_centrality, closeness_centrality
+
+    Notes
+    -----
+    If the 'distance' keyword is set to an edge attribute key then the
+    shortest-path length will be computed using Dijkstra's algorithm with
+    that edge attribute as the edge weight.
+
+    References
+    ----------
+    .. [1] Boldi, Paolo, and Sebastiano Vigna. "Axioms for centrality."
+           Internet Mathematics 10.3-4 (2014): 222-262.
+    """
+    if G.is_directed():
+        G = G.reverse()
+    spl = partial(nx.shortest_path_length, G, weight=distance)
+    return {
+        u: sum(1 / d if d > 0 else 0 for v, d in spl(source=u).items())
+        for u in G.nbunch_iter(nbunch)
+    }