Source code for cfpq_data.graphs.generators.labeled_barabasi_albert_graph
"""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.
"""
import logging
import random
from typing import List, Union, Callable
import networkx as nx
__all__ = ["labeled_barabasi_albert_graph"]
[docs]
def labeled_barabasi_albert_graph(
n: int,
m: int,
*,
labels: List[str] = "abcd",
choice: Callable[[List[str]], str] = random.choice,
seed: Union[int, None] = None,
) -> nx.MultiDiGraph:
"""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 : MultiDiGraph
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
"""
graph = nx.MultiDiGraph(nx.barabasi_albert_graph(n=n, m=m, seed=seed))
random.seed(seed)
for edge in graph.edges:
graph.edges[edge]["label"] = choice(labels)
logging.info(
f"Create a random {graph=} "
f"according to the Barabási–Albert preferential attachment model "
f"with {n=}, {m=}, {labels=}, {choice=}, {seed=}"
)
return graph
