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 CirculatedNeighborsRandomWalkSampler(Sampler):
r"""An implementation of circulated neighbor random walk sampling. The process
simulates a random walker. Vertices of a neighbourhood are randomly reshuffled
after all of them is sampled from the vicinity of a node. This way the walker
can escape closely knit communities. `"For details about the algorithm see
this paper." <https://dl.acm.org/doi/10.5555/2794367.2794373>`_
Args:
number_of_nodes (int): Number of sampled nodes. Default is 100.
seed (int): Random seed. Default is 42.
"""
def __init__(self, number_of_nodes: int = 100, seed: int = 42):
self.number_of_nodes = number_of_nodes
self.seed = seed
self._set_seed()
def _create_node_set(self, graph, start_node):
"""
Choosing an initial node.
"""
if start_node is not None:
if start_node >= 0 and start_node < self.backend.get_number_of_nodes(graph):
self._current_node = start_node
self._sampled_nodes = set([self._current_node])
else:
raise ValueError("Starting node index is out of range.")
else:
self._current_node = random.choice(
range(self.backend.get_number_of_nodes(graph))
)
self._sampled_nodes = set([self._current_node])
def _do_shuffling(self, graph, node):
"""
Shuffling the neighbors of a node in the circulated map.
Arg types:
* **node** *(int)* - The node considered.
"""
self._circulated_map[node] = self.backend.get_neighbors(graph, node)
random.shuffle(self._circulated_map[node])
def _create_circulated_map(self, graph):
"""
Creating an initial random shuffle node-neighbor map.
"""
self._circulated_map = {}
for node in self.backend.get_node_iterator(graph):
self._do_shuffling(graph, node)
def _make_a_step(self, graph):
"""
Doing a single step of the circulated neighbor random walk.
"""
if len(self._circulated_map[self._current_node]) == 0:
self._do_shuffling(graph, self._current_node)
self._current_node = self._circulated_map[self._current_node].pop()
self._sampled_nodes.add(self._current_node)
[docs] def sample(
self, graph: Union[NXGraph, NKGraph], start_node: int = None
) -> Union[NXGraph, NKGraph]:
"""
Sampling nodes iteratively with a circulated neighbor random walk sampler.
Arg types:
* **graph** *(NetworkX or NetworKit graph)* - The graph to be sampled from.
* **start_node** *(int, optional)* - The start node.
Return types:
* **new_graph** *(NetworkX or NetworKit graph)* - The graph of sampled nodes.
"""
self._deploy_backend(graph)
self._check_number_of_nodes(graph)
self._create_node_set(graph, start_node)
self._create_circulated_map(graph)
while len(self._sampled_nodes) < self.number_of_nodes:
self._make_a_step(graph)
new_graph = self.backend.get_subgraph(graph, self._sampled_nodes)
return new_graph