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"""
Current-flow betweenness centrality measures for subsets of nodes.
"""
# Copyright (C) 2010 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
__all__ = ['current_flow_betweenness_centrality_subset',
'edge_current_flow_betweenness_centrality_subset']
import networkx as nx
def current_flow_betweenness_centrality_subset(G,sources,targets,
normalized=True):
"""Compute current-flow betweenness centrality for subsets 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
If True the betweenness values are normalized by b=b/(n-1)(n-2) where
n is the number of nodes in G.
Returns
-------
nodes : dictionary
Dictionary of nodes with betweenness centrality as the value.
See Also
--------
betweenness_centrality
edge_betweenness_centrality
edge_current_flow_betweenness_centrality
Notes
-----
The algorithm is from Brandes [1]_.
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://www.inf.uni-konstanz.de/algo/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_subset() requires NumPy
http://scipy.org/""")
if G.is_directed():
raise nx.NetworkXError(\
"current_flow_betweenness_centrality_subset() not defined for digraphs.")
if not nx.is_connected(G):
raise nx.NetworkXError("Graph not connected.")
betweenness=dict.fromkeys(G,0.0) # b[v]=0 for v in G
F=_compute_F(G) # Current-flow matrix
m,n=F.shape # m edges and n nodes
mapping=dict(zip(G,xrange(n))) # map nodes to integers
for (ei,e) in enumerate(G.edges_iter()):
u,v=e
# ei is index of edge
Fe=F[ei,:] # ei row of F
for s in sources:
i=mapping[s]
for t in targets:
j=mapping[t]
betweenness[u]+=0.5*np.abs(Fe[i]-Fe[j])
betweenness[v]+=0.5*np.abs(Fe[i]-Fe[j])
if normalized:
nb=(n-1.0)*(n-2.0) # normalization factor
else:
nb=2.0
for v in G:
betweenness[v]=betweenness[v]/nb+1.0/(2-n)
return betweenness
def edge_current_flow_betweenness_centrality_subset(G,sources,targets,
normalized=True):
"""Compute edge 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
If True the betweenness values are normalized by b=b/(n-1)(n-2) where
n is the number of nodes in G.
Returns
-------
nodes : dictionary
Dictionary of edge tuples with betweenness centrality as the value.
See Also
--------
betweenness_centrality
edge_betweenness_centrality
current_flow_betweenness_centrality
Notes
-----
The algorithm is from Brandes [1]_.
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://www.inf.uni-konstanz.de/algo/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_subset() requires NumPy
http://scipy.org/""")
if G.is_directed():
raise nx.NetworkXError(\
"edge_current_flow_closeness_centrality_subset() not defined for digraphs.")
if not nx.is_connected(G):
raise nx.NetworkXError("Graph not connected.")
betweenness=(dict.fromkeys(G.edges(),0.0))
F=_compute_F(G) # Current-flow matrix
m,n=F.shape # m edges and n nodes
if normalized:
nb=(n-1.0)*(n-2.0) # normalization factor
else:
nb=2.0
mapping=dict(zip(G,xrange(n))) # map nodes to integers
for (ei,e) in enumerate(G.edges_iter()):
# ei is index of edge
Fe=F[ei,:] # ei row of F
for s in sources:
i=mapping[s]
for t in targets:
j=mapping[t]
betweenness[e]+=0.5*np.abs(Fe[i]-Fe[j])
betweenness[e]/=nb
return betweenness
def _compute_C(G):
"""Inverse of Laplacian."""
try:
import numpy as np
except ImportError:
raise ImportError(
"""current_flow_betweenness_centrality_subset() requires NumPy
http://scipy.org/""")
L=nx.laplacian(G) # use ordering of G.nodes()
# remove first row and column
LR=L[1:,1:]
LRinv=np.linalg.inv(LR)
C=np.zeros(L.shape)
C[1:,1:]=LRinv
return C
def _compute_F(G):
"""Current flow matrix."""
try:
import numpy as np
except ImportError:
raise ImportError(
"""current_flow_betweenness_centrality_subset() requires NumPy
http://scipy.org/""")
C=np.asmatrix(_compute_C(G))
n=G.number_of_nodes()
m=G.number_of_edges()
B=np.zeros((n,m))
# use G.nodes() and G.edges() ordering of edges for B
mapping=dict(zip(G,xrange(n))) # map nodes to integers
for (ei,(v,w,d)) in enumerate(G.edges_iter(data=True)):
c=d.get('weight',1.0)
vi=mapping[v]
wi=mapping[w]
B[vi,ei]=c
B[wi,ei]=-c
return np.asarray(B.T*C)
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