Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
76 lines
2.2 KiB
76 lines
2.2 KiB
4 years ago
|
"""
|
||
|
|
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}
|