NetworkX包中似乎沒有現成的對圖洗牌的函數
寫一個，如下
'''
shuffle_graph - This is a graph shuffling package.
Copyright (C) 2019 sosei
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
'''
__all__ = ['calculate_number_of_shuffles_required_under_default_random_function', 'shuffle_graph']
def calculate_number_of_shuffles_required_under_default_random_function(node_number: int) -> int:
'''
Python's random number function USES the Mersenne Twister algorithm, which has a period of 2**19937-1.If the total permutation of graph nodes is larger than the random function period, the card cannot be shuffled only once.
The total permutation of a graph node is "factorial of the number of nodes".The number of binary digits of the total number of permutations can be calculated by Stirling's formula.
>>> calculate_number_of_shuffles_required_under_default_random_function(1000)
1
>>> calculate_number_of_shuffles_required_under_default_random_function(10000)
6
'''
import math
if node_number > 0:
bit_length_of_permutation_number = math.ceil(math.log2(2*math.pi*node_number)/2 + math.log2(node_number/math.e)*node_number) #圖節點的全排列量是“節點數的階乘”。而全排列量的二進制位數可用Stirling公式算出
shuffle_number = math.ceil(bit_length_of_permutation_number/19937) #Python的隨機數函數使用Mersenne Twister算法，它週期爲2**19937-1。圖節點全排列量大於隨即發生函數週期的話就不能只洗牌一遍
else:
shuffle_number = 0
return shuffle_number
def shuffle_graph(data_graph: 'NetworkXGraphObject', shuffle_number: int, seed: int = None) -> 'NetworkXGraphObject':
'''
Returns a new graph, shuffling the order of the nodes in the input data_graph, but the relationship between the nodes remains the same. The data_graph doesn't change.
Parameters
----------
data_graph : NetworkXGraphObject
A NetworkX graph object.
shuffle_number : integer
Set the number of shuffles.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
Returns
-------
new_order_data_graph : NetworkXGraphObject
Returns a new graph that shuffles the order of nodes but keeps the relationships between them the same.
Examples
--------
>>> from networkx.classes.graph import Graph
>>> G = Graph({0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}, 3: {}}, 3: {2: {}, 4: {}}, 4: {3: {}}})
>>> shuffle_graph(G, 1, 65535).adj #Set seed to make the results repeatable.
AdjacencyView({3: {2: {}, 4: {}}, 4: {3: {}}, 1: {0: {}, 2: {}}, 2: {3: {}, 1: {}}, 0: {1: {}}})
'''
import random
from networkx.classes.graph import Graph
from networkx.classes.digraph import DiGraph
from networkx.classes.multigraph import MultiGraph
from networkx.classes.multidigraph import MultiDiGraph
from networkx.convert import from_dict_of_dicts
random.seed(seed)
list_of_nodes = list(data_graph.nodes)
for _i in range(shuffle_number):
random.shuffle(list_of_nodes)
new_order_data_graph = dict()
for node in list_of_nodes:
new_order_data_graph.update({node: data_graph[node]})
if data_graph.is_directed():
if data_graph.is_multigraph():
new_order_data_graph = from_dict_of_dicts(new_order_data_graph, create_using = MultiDiGraph, multigraph_input = True)
else:
new_order_data_graph = from_dict_of_dicts(new_order_data_graph, create_using = DiGraph)
else:
if data_graph.is_multigraph():
new_order_data_graph = from_dict_of_dicts(new_order_data_graph, create_using = MultiGraph, multigraph_input = True)
else:
new_order_data_graph = from_dict_of_dicts(new_order_data_graph, create_using = Graph)
return new_order_data_graph
對圖的洗牌就是改變圖數據結構中節點的順序，節點間的邊關係不變
>>> from networkx.classes.graph import Graph
>>> G = Graph({0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}, 3: {}}, 3: {2: {}, 4: {}}, 4: {3: {}}})
>>> shuffle_graph(G, 1).adj
AdjacencyView({1: {0: {}, 2: {}}, 4: {3: {}}, 2: {1: {}, 3: {}}, 3: {4: {}, 2: {}}, 0: {1: {}}})
這樣的
已經發布到了PyPI上，可以很方便的安裝分發了
pip install shuffle-graph
程序裏引用：
from shuffle_graph_package import *