Ctrl+K

cfpq_data.graphs.generators.labeled_barabasi_albert_graph#

labeled_barabasi_albert_graph(n: int, m: int, *, labels: ~typing.List[str] = 'abcd', choice: ~typing.Callable[[~typing.List[str]], str] = <bound method Random.choice of <random.Random object>>, seed: int | None = None) → MultiDiGraph[source]#

Returns a random graph according to the Barabási–Albert preferential attachment model. With labeled edges.

A graph of n nodes is grown by attaching new nodes each with m edges that are preferentially attached to existing nodes with high degree.

Parameters:

  • n (int) -- Number of nodes.

  • m (int) -- Number of edges to attach from a new node to existing nodes.

  • labels (Iterable[str]) -- Labels that will be used to mark the edges of the graph.

  • choice (Callable[[Iterable[str]], str]) -- Function for marking edges.

  • seed (Union[int, RandomState, None]) -- Indicator of random number generation state.

Examples

>>> from cfpq_data import *
>>> g = labeled_barabasi_albert_graph(42, 29, seed=42)
>>> g.number_of_nodes()
42
>>> g.number_of_edges()
754
Returns:

g -- A random graph according to the Barabási–Albert preferential attachment model.

Return type:

MultiDiGraph

Raises:

NetworkXError -- If m does not satisfy 1 <= m < n.

References

[1]

A. L. Barabási and R. Albert "Emergence of scaling in random networks", Science 286, pp 509-512, 1999.

On this page