import random
import networkx as nx
import networkit as nk
from typing import Union
from queue import LifoQueue
from littleballoffur.sampler import Sampler
NKGraph = type(nk.graph.Graph())
NXGraph = nx.classes.graph.Graph
[docs]class DepthFirstSearchSampler(Sampler):
r"""An implementation of node sampling by depth first search. The starting node
is selected randomly and neighbors are added to the last in first out queue
by shuffling them randomly.
Args:
number_of_nodes (int): Number of 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_seed_set(self, graph, start_node):
"""
Creating a visited node set and a traversal path list.
"""
self._queue = LifoQueue()
if start_node is not None:
if start_node >= 0 and start_node < self.backend.get_number_of_nodes(graph):
self._queue.put(start_node)
else:
raise ValueError("Starting node index is out of range.")
else:
start_node = random.choice(range(self.backend.get_number_of_nodes(graph)))
self._queue.put(start_node)
self._nodes = set()
self._path = []
def _extract_edges(self):
"""
Extracting edges from the depth first search tree.
"""
self._edges = [
[self._path[i], self._path[i + 1]] for i in range(len(self._path) - 1)
]
[docs] def sample(
self, graph: Union[NXGraph, NKGraph], start_node: int = None
) -> Union[NXGraph, NKGraph]:
"""
Sampling a graph with randomized depth first search.
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_seed_set(graph, start_node)
while len(self._nodes) < self.number_of_nodes:
source = self._queue.get()
if source not in self._nodes:
neighbors = self.backend.get_neighbors(graph, source)
random.shuffle(neighbors)
for neighbor in neighbors:
self._queue.put(neighbor)
self._nodes.add(source)
self._path.append(source)
self._extract_edges()
if len(self._edges) > 0:
new_graph = self.backend.graph_from_edgelist(self._edges)
new_graph = self.backend.get_subgraph(new_graph, self._nodes)
else:
new_graph = self.backend.get_subgraph(graph, self._nodes)
return new_graph