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# Source code for networkx.algorithms.centrality.current_flow_betweenness_subset

#    Copyright (C) 2010-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#
# Author: Aric Hagberg (hagberg@lanl.gov)
"""Current-flow betweenness centrality measures for subsets of nodes."""
import itertools

import networkx as nx
from networkx.algorithms.centrality.flow_matrix import *
from networkx.utils import not_implemented_for, reverse_cuthill_mckee_ordering

__all__ = ['current_flow_betweenness_centrality_subset',
'edge_current_flow_betweenness_centrality_subset']

[docs]@not_implemented_for('directed')
def current_flow_betweenness_centrality_subset(G, sources, targets,
normalized=True,
weight=None,
dtype=float, solver='lu'):
r"""Compute current-flow betweenness centrality for subsets of nodes.

Current-flow betweenness centrality uses an electrical current
model for information spreading in contrast to betweenness
centrality which uses shortest paths.

Current-flow betweenness centrality is also known as
random-walk betweenness centrality [2]_.

Parameters
----------
G : graph
A NetworkX graph

sources: list of nodes
Nodes to use as sources for current

targets: list of nodes
Nodes to use as sinks for current

normalized : bool, optional (default=True)
If True the betweenness values are normalized by b=b/(n-1)(n-2) where
n is the number of nodes in G.

weight : string or None, optional (default=None)
Key for edge data used as the edge weight.
If None, then use 1 as each edge weight.

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 betweenness centrality as the value.

--------
approximate_current_flow_betweenness_centrality
betweenness_centrality
edge_betweenness_centrality
edge_current_flow_betweenness_centrality

Notes
-----
Current-flow betweenness can be computed in $O(I(n-1)+mn \log n)$
time [1]_, where $I(n-1)$ is the time needed to compute the
inverse Laplacian.  For a full matrix this is $O(n^3)$ but using
sparse methods you can achieve $O(nm{\sqrt k})$ where $k$ is the
Laplacian matrix condition number.

The space required is $O(nw)$ where $w$ is the width of the sparse
Laplacian matrix.  Worse case is $w=n$ for $O(n^2)$.

If the edges have a 'weight' attribute they will be used as
weights in this algorithm.  Unspecified weights are set to 1.

References
----------
.. [1] Centrality Measures Based on Current Flow.
Ulrik Brandes and Daniel Fleischer,
Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
http://algo.uni-konstanz.de/publications/bf-cmbcf-05.pdf

.. [2] A measure of betweenness centrality based on random walks,
M. E. J. Newman, Social Networks 27, 39-54 (2005).
"""
from networkx.utils import reverse_cuthill_mckee_ordering
try:
import numpy as np
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires NumPy ',
'http://scipy.org/')
try:
import scipy
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires SciPy ',
'http://scipy.org/')
if not nx.is_connected(G):
raise nx.NetworkXError("Graph not connected.")
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
mapping = dict(zip(ordering, range(n)))
H = nx.relabel_nodes(G, mapping)
betweenness = dict.fromkeys(H, 0.0)  # b[v]=0 for v in H
for row, (s, t) in flow_matrix_row(H, weight=weight, dtype=dtype,
solver=solver):
for ss in sources:
i = mapping[ss]
for tt in targets:
j = mapping[tt]
betweenness[s] += 0.5 * np.abs(row[i] - row[j])
betweenness[t] += 0.5 * np.abs(row[i] - row[j])
if normalized:
nb = (n - 1.0) * (n - 2.0)  # normalization factor
else:
nb = 2.0
for v in H:
betweenness[v] = betweenness[v] / nb + 1.0 / (2 - n)
return dict((ordering[k], v) for k, v in betweenness.items())

[docs]@not_implemented_for('directed')
def edge_current_flow_betweenness_centrality_subset(G, sources, targets,
normalized=True,
weight=None,
dtype=float, solver='lu'):
r"""Compute current-flow betweenness centrality for edges using subsets
of nodes.

Current-flow betweenness centrality uses an electrical current
model for information spreading in contrast to betweenness
centrality which uses shortest paths.

Current-flow betweenness centrality is also known as
random-walk betweenness centrality [2]_.

Parameters
----------
G : graph
A NetworkX graph

sources: list of nodes
Nodes to use as sources for current

targets: list of nodes
Nodes to use as sinks for current

normalized : bool, optional (default=True)
If True the betweenness values are normalized by b=b/(n-1)(n-2) where
n is the number of nodes in G.

weight : string or None, optional (default=None)
Key for edge data used as the edge weight.
If None, then use 1 as each edge weight.

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 : dict
Dictionary of edge tuples with betweenness centrality as the value.

--------
betweenness_centrality
edge_betweenness_centrality
current_flow_betweenness_centrality

Notes
-----
Current-flow betweenness can be computed in $O(I(n-1)+mn \log n)$
time [1]_, where $I(n-1)$ is the time needed to compute the
inverse Laplacian.  For a full matrix this is $O(n^3)$ but using
sparse methods you can achieve $O(nm{\sqrt k})$ where $k$ is the
Laplacian matrix condition number.

The space required is $O(nw)$ where $w$ is the width of the sparse
Laplacian matrix.  Worse case is $w=n$ for $O(n^2)$.

If the edges have a 'weight' attribute they will be used as
weights in this algorithm.  Unspecified weights are set to 1.

References
----------
.. [1] Centrality Measures Based on Current Flow.
Ulrik Brandes and Daniel Fleischer,
Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05).
LNCS 3404, pp. 533-544. Springer-Verlag, 2005.
http://algo.uni-konstanz.de/publications/bf-cmbcf-05.pdf

.. [2] A measure of betweenness centrality based on random walks,
M. E. J. Newman, Social Networks 27, 39-54 (2005).
"""
try:
import numpy as np
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires NumPy ',
'http://scipy.org/')
try:
import scipy
except ImportError:
raise ImportError('current_flow_betweenness_centrality requires SciPy ',
'http://scipy.org/')
if not nx.is_connected(G):
raise nx.NetworkXError("Graph not connected.")
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
mapping = dict(zip(ordering, range(n)))
H = nx.relabel_nodes(G, mapping)
edges = (tuple(sorted((u, v))) for u, v in H.edges())
betweenness = dict.fromkeys(edges, 0.0)
if normalized:
nb = (n - 1.0) * (n - 2.0)  # normalization factor
else:
nb = 2.0
for row, (e) in flow_matrix_row(H, weight=weight, dtype=dtype,
solver=solver):
for ss in sources:
i = mapping[ss]
for tt in targets:
j = mapping[tt]
betweenness[e] += 0.5 * np.abs(row[i] - row[j])
betweenness[e] /= nb
return dict(((ordering[s], ordering[t]), v)
for (s, t), v in betweenness.items())

# fixture for nose tests
def setup_module(module):
from nose import SkipTest
try:
import numpy
import scipy
except:
raise SkipTest("NumPy not available")