import random
import networkx as nx
import networkit as nk
from typing import Union
from littleballoffur.sampler import Sampler
NKGraph = type(nk.graph.Graph())
NXGraph = nx.classes.graph.Graph
[docs]class RandomNodeEdgeSampler(Sampler):
r"""An implementation of random node-edge sampling. The algorithm first randomly
samples a node. From this node it samples an edge with a neighbor. `"For details about the algorithm see
this paper." <http://www.cs.ucr.edu/~michalis/PAPERS/sampling-networking-05.pdf>`_
Args:
number_of_edges (int): Number of edges. Default is 100.
seed (int): Random seed. Default is 42.
"""
def __init__(self, number_of_edges: int = 100, seed: int = 42):
self.number_of_edges = number_of_edges
self.seed = seed
self._set_seed()
def _create_initial_edge_set(self, graph: Union[NXGraph, NKGraph]):
"""
Choosing initial edges.
"""
self._sampled_edges = set()
while len(self._sampled_edges) < self.number_of_edges:
source_node = random.choice(range(self.backend.get_number_of_nodes(graph)))
target_node = random.choice(self.backend.get_neighbors(graph, source_node))
edge = sorted([source_node, target_node])
edge = tuple(edge)
self._sampled_edges.add(edge)
[docs] def sample(self, graph: Union[NXGraph, NKGraph]) -> Union[NXGraph, NKGraph]:
"""
Sampling edges randomly from randomly sampled nodes.
Arg types:
* **graph** *(NetworkX or NetworKit graph)* - The graph to be sampled from.
Return types:
* **new_graph** *(NetworkX or NetworKit graph)* - The graph of sampled edges.
"""
self._deploy_backend(graph)
self._check_number_of_edges(graph)
self._create_initial_edge_set(graph)
new_graph = self.backend.graph_from_edgelist(self._sampled_edges)
return new_graph