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:

nint: Number of nodes.
mint: 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.
seedint or None: Indicator of random number generation state.

Returns:

gMultiDiGraph: A random graph according to the Barabási–Albert preferential attachment model.

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.

[2]

https://networkx.org/documentation/stable//reference/randomness.html#randomness

Examples

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