在networkx中添加和删除随机边缘

I'm using NetworkX in python. Given any undirected and unweighted graph, I want to loop through all the nodes. With each node, I want to add a random edge and/or delete an existing random edge for that node with probability p. Is there a simple way to do this? Thanks a lot!

我在python中使用NetworkX。给定任何无向和未加权的图形，我想循环遍历所有节点。对于每个节点，我想添加随机边缘和/或以概率p删除该节点的现有随机边缘。有一个简单的方法吗？非常感谢！

2 个解决方案

#1

Given a node i, To add edges without duplication you need to know (1) what edges from i already exist and then compute (2) the set of candidate edges that don't exist from i. For removals, you already defined a method in the comment - which is based simply on (1). Here is a function that will provide one round of randomised addition and removal, based on list comprehensions

给定节点i，要添加没有重复的边缘，您需要知道（1）来自i的边缘已经存在，然后计算（2）i中不存在的候选边缘集合。对于删除，您已在注释中定义了一个方法 - 该方法仅基于（1）。这是一个基于列表推导提供一轮随机添加和删除的功能

def add_and_remove_edges(G, p_new_connection, p_remove_connection):    
    '''    
    for each node,    
      add a new connection to random other node, with prob p_new_connection,    
      remove a connection, with prob p_remove_connection    

    operates on G in-place    
    '''                
    new_edges = []    
    rem_edges = []    

    for node in G.nodes():    
        # find the other nodes this one is connected to    
        connected = [to for (fr, to) in G.edges(node)]    
        # and find the remainder of nodes, which are candidates for new edges   
        unconnected = [n for n in G.nodes() if not n in connected]    

        # probabilistically add a random edge    
        if len(unconnected): # only try if new edge is possible    
            if random.random() < p_new_connection:    
                new = random.choice(unconnected)    
                G.add_edge(node, new)    
                print "\tnew edge:\t {} -- {}".format(node, new)    
                new_edges.append( (node, new) )    
                # book-keeping, in case both add and remove done in same cycle  
                unconnected.remove(new)    
                connected.append(new)    

        # probabilistically remove a random edge    
        if len(connected): # only try if an edge exists to remove    
            if random.random() < p_remove_connection:    
                remove = random.choice(connected)    
                G.remove_edge(node, remove)    
                print "\tedge removed:\t {} -- {}".format(node, remove)    
                rem_edges.append( (node, remove) )    
                # book-keeping, in case lists are important later?    
                connected.remove(remove)    
                unconnected.append(remove)    
    return rem_edges, new_edges

To see this function in action:

要查看此功能的实际效果：

import networkx as nx
import random
import matplotlib.pyplot as plt

p_new_connection = 0.1
p_remove_connection = 0.1

G = nx.karate_club_graph() # sample graph (undirected, unweighted)
# show original
plt.figure(1); plt.clf()
fig, ax = plt.subplots(2,1, num=1, sharex=True, sharey=True)
pos = nx.spring_layout(G)
nx.draw_networkx(G, pos=pos, ax=ax[0])

# now apply one round of changes
rem_edges, new_edges = add_and_remove_edges(G, p_new_connection, p_remove_connection)

# and draw new version and highlight changes
nx.draw_networkx(G, pos=pos, ax=ax[1])
nx.draw_networkx_edges(G, pos=pos, ax=ax[1], edgelist=new_edges,
                       edge_color='b', width=4)
# note: to highlight edges that were removed, add them back in;
# This is obviously just for display!
G.add_edges_from(rem_edges)
nx.draw_networkx_edges(G, pos=pos, ax=ax[1], edgelist=rem_edges,
                       edge_color='r', style='dashed', width=4)
G.remove_edges_from(rem_edges)

plt.show()

And you should see something like this.

你应该看到这样的事情。

Note that you could also do something similar with the adjacency matrix, A = nx.adjacency_matrix(G).todense() (it's a numpy matrix so operations like A[i,:].nonzero() would be relevant). This might be more efficient if you have extremely large networks.

请注意，您也可以使用邻接矩阵A = nx.adjacency_matrix（G）.todense（）执行类似的操作（它是一个numpy矩阵，因此像A [i，：]。nonzero（）这样的操作是相关的）。如果您拥有极大的网络，这可能会更有效。

2 个解决方案

#1

更多相关文章

随机推荐