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"""Current-flow closeness centrality measures.
"""
# Copyright (C) 2010-2013 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
import networkx as nx
from networkx.algorithms.centrality.flow_matrix import *
__author__ = """Aric Hagberg <aric.hagberg@gmail.com>"""
__all__ = ['current_flow_closeness_centrality', 'information_centrality']
def current_flow_closeness_centrality(G, weight='weight',
dtype=float, solver='lu'):
"""Compute current-flow closeness centrality for nodes.
A variant of closeness centrality based on effective
resistance between nodes in a network. This metric
is also known as information centrality.
Parameters
----------
G : graph
A NetworkX graph
dtype: data type (float)
Default data type for internal matrices.
Set to np.float32 for lower memory consumption.
solver: string (default='lu')
Type of linear solver to use for computing the flow matrix.
Options are "full" (uses most memory), "lu" (recommended), and
"cg" (uses least memory).
Returns
-------
nodes : dictionary
Dictionary of nodes with current flow closeness centrality as the value.
See Also
--------
closeness_centrality
Notes
-----
The algorithm is from Brandes [1]_.
See also [2]_ for the original definition of information centrality.
References
----------
.. [1] Ulrik Brandes and Daniel Fleischer,
Centrality Measures Based on Current Flow.
Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf
.. [2] Stephenson, K. and Zelen, M.
Rethinking centrality: Methods and examples.
Social Networks. Volume 11, Issue 1, March 1989, pp. 1-37
http://dx.doi.org/10.1016/0378-8733(89)90016-6
"""
from networkx.utils import reverse_cuthill_mckee_ordering
import numpy as np
import scipy
if G.is_directed():
raise nx.NetworkXError(
"current_flow_closeness_centrality() not defined for digraphs.")
if not nx.is_connected(G):
raise nx.NetworkXError("Graph not connected.")
solvername = {"full": FullInverseLaplacian,
"lu": SuperLUInverseLaplacian,
"cg": CGInverseLaplacian}
n = G.number_of_nodes()
ordering = list(reverse_cuthill_mckee_ordering(G))
# make a copy with integer labels according to rcm ordering
# this could be done without a copy if we really wanted to
H = nx.relabel_nodes(G, dict(zip(ordering, range(n))))
betweenness = dict.fromkeys(H, 0.0) # b[v]=0 for v in H
n = H.number_of_nodes()
L = laplacian_sparse_matrix(H, nodelist=range(n), weight=weight,
dtype=dtype, format='csc')
C2 = solvername[solver](L, width=1, dtype=dtype) # initialize solver
for v in H:
col = C2.get_row(v)
for w in H:
betweenness[v] += col[v]-2*col[w]
betweenness[w] += col[v]
for v in H:
betweenness[v] = 1.0 / (betweenness[v])
return dict((ordering[k], float(v)) for k, v in betweenness.items())
information_centrality = current_flow_closeness_centrality
# fixture for nose tests
def setup_module(module):
from nose import SkipTest
try:
import numpy
except:
raise SkipTest("NumPy not available")
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