"""Current-flow betweenness centrality measures for subsets of nodes."""
import networkx as nx
from networkx.algorithms.centrality.flow_matrix import flow_matrix_row
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.
The weight reflects the capacity or the strength of the
edge.
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.
See Also
--------
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.
https://doi.org/10.1007/978-3-540-31856-9_44
.. [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
import numpy as np
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 {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.
The weight reflects the capacity or the strength of the
edge.
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.
See Also
--------
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.
https://doi.org/10.1007/978-3-540-31856-9_44
.. [2] A measure of betweenness centrality based on random walks,
M. E. J. Newman, Social Networks 27, 39-54 (2005).
"""
import numpy as np
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 {(ordering[s], ordering[t]): v for (s, t), v in betweenness.items()}