navis.betweeness_centrality

navis.betweeness_centrality(x, from_=None, directed=True)

Calculate vertex/node betweenness.

Betweenness is (roughly) defined by the number of shortest paths going through a vertex or an edge.

Parameters:

• x (TreeNeuron | MeshNeuron | NeuronList) –

• from ("leafs" | "branch_points" | iterable, optional) –

  If provided will only consider paths from given nodes to root(s):

  • leafs will only use paths from leafs to the root

  • branch_points will only use paths from branch points to the root

  • from_ can also be a list/array of node IDs

  Only implemented for directed=True!

• directed (bool) – Whether to use the directed or undirected graph.

• parallel (bool) – If True and input is NeuronList, use parallel processing. Requires pathos.

• n_cores (int, optional) – Numbers of cores to use if parallel=True. Defaults to half the available cores.

• progress (bool) – Whether to show a progress bar. Overruled by navis.set_pbars.

• omit_failures (bool) – If True will omit failures instead of raising an exception. Ignored if input is single neuron.

Returns:

Adds “betweenness” as column in the node table (for TreeNeurons) or as .betweenness property (for MeshNeurons).

Return type:

neuron

Examples

>>> import navis
>>> n = navis.example_neurons(2, kind='skeleton')
>>> n.reroot(n.soma, inplace=True)
>>> _ = navis.betweeness_centrality(n)
>>> n[0].nodes.betweenness.max()
436866
>>> m = navis.example_neurons(1, kind='mesh')
>>> _ = navis.betweeness_centrality(m)
>>> m.betweenness.max()
59637