approximate_current_flow_betweenness_centrality¶
- approximate_current_flow_betweenness_centrality(G, normalized=True, weight='weight', dtype=<type 'float'>, solver='lu', epsilon=0.5, kmax=10000)¶
Compute the approximate current-flow betweenness centrality for nodes.
Approximates the current-flow betweenness centrality within absolute error of epsilon with high probability [R129].
Parameters : G : graph
A NetworkX graph
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=’weight’)
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).
epsilon: float :
Absolute error tolerance.
kmax: int :
Maximum number of sample node pairs to use for approximation.
Returns : nodes : dictionary
Dictionary of nodes with betweenness centrality as the value.
See also
Notes
The running time is
and the space required is 
for n nodes and m edges.If the edges have a ‘weight’ attribute they will be used as weights in this algorithm. Unspecified weights are set to 1.
References
[R129] (1, 2) 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