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approximate_current_flow_betweenness_centrality(G, normalized=True, weight=None, dtype=<class 'float'>, solver='full', epsilon=0.5, kmax=10000, seed=None)

Approximates the current-flow betweenness centrality within absolute error of epsilon with high probability .

Notes

The running time is $O((1/\epsilon^2)m{\sqrt k} \log n)$ and the space required is $O(m)$ 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.

Parameters

G : graph: A NetworkX graph
normalized : bool, optional (default=True): If True the betweenness values are normalized by 2/[(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='full'): 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.
seed : integer, random_state, or None (default): Indicator of random number generation state. See Randomness<randomness> .

Returns

nodes : dictionary: Dictionary of nodes with betweenness centrality as the value.

Compute the approximate current-flow betweenness centrality for nodes.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.algorithms.centrality.current_flow_betweenness.current_flow_betweenness_centrality networkx.algorithms.centrality.current_flow_betweenness_subset.current_flow_betweenness_centrality_subset

Local connectivity graph

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All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them