python中顶点列表（不是两个顶点）之间的距离

Question

我之前在 igraph 中使用了函数distance()，它计算两个节点或两个节点向量之间的距离。现在我正在使用NetworkX 2.2 在python 中编写代码，并且我还试图找到两个节点列表之间的距离（不是两个节点）。

似乎在 NetworkX 中没有功能可以做到这一点。事实上，我使用了shortest_path_length()，但它没有用。我在这里做的是：1.逐条读取图形，2.然后为每条边选择第一个顶点v1和第二个顶点v2，3.找到连接到第一个顶点的邻居并找到连接的邻居到第二个顶点，4.最后计算v1的邻居和v2的邻居之间的距离。

最后我想得到，对于每条边，一个向量包含顶点 v1 和 v2 的邻居之间的距离。我在 R 中的代码

library(igraph)
graph<-matrix(c(4,3,4,1,4,2,3,2,3,1),ncol=2,byrow=TRUE)
g<-graph.data.frame(d = graph, directed = FALSE)

v1<-c()
v2<-c()
n1<-list()
n2<-list()
distance<-list()
distance.bt.neighbors<-list()

for(edge in 1:length(E(g))){
  v1[edge]<-ends(graph = g, es = edge)[1]
  v2[edge]<-ends(graph = g, es = edge)[2]
  n1<-neighbors(g,v1[edge],mode=c("all"))
  n2<-neighbors(g,v2[edge],mode=c("all"))
  distance[[edge]]<-distances(g, v = n1, to = n2, mode = c("all"))
  distance.bt.neighbors[[edge]]<-c(distance[[edge]])
                           }
distance.bt.neighbors
[[1]]
[1] 1 1 1 1 0 2 1 2 0

[[2]]
[1] 1 1 1 0 1 1

[[3]]
[1] 1 1 1 0 1 1

[[4]]
[1] 0 1 1 1 1 1

[[5]]
[1] 0 1 1 1 1 1

为了在python中做到这一点，我写了这段代码

import os
import igraph
import numpy as np
import networkx as nx

os.chdir('Desktop')

graph = nx.read_edgelist("attempt") # the file attempt contains the same data as in the R code.

neighbor1 = []
neighbor2 = []
distance = []

for edge in list(graph.edges):
  neighbor1.append(list(graph.neighbors(edge[0])))
  neighbor2.append(list(graph.neighbors(edge[1])))
  distance.append(nx.shortest_path_length(graph, source=neighbor1, target= neighbor2))

但我收到此错误，指出邻居未定义为顶点，因为它们不是单个值的列表

Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "/home/abdelrahman/anaconda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/generic.py", line 312, in shortest_path_length
p = nx.bidirectional_shortest_path(G, source, target)
File "/home/abdelrahman/anaconda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/unweighted.py", line 223, in bidirectional_shortest_path
raise nx.NodeNotFound(msg.format(source, target))
networkx.exception.NodeNotFound: Either source [['3', '4']] or target [['1', '2']] is not in G

在 python 中是否有可能获得一个顶点列表之间的距离列表，而不是像我在 R 中所做的那样的单个值？有没有这样的功能，如果没有，是否可以修改当前的功能？

注意：我没有使用 igraph-python 来获取所需的列表，原因有两个：根据我的搜索，在 igraph 中没有这样的功能，以及避免丢失 tryinh 时产生的顶点名称的问题获取顶点的邻居。

Answer 1

给出错误的代码的最后一行。 在Forneighbor1 和neighbor2 内部在每次迭代后更新为具有多个节点的列表，对于nx.shortest_path_length，您需要传递单个源和单个目标节点，而不是列表。 我希望这会有所帮助。

更新

这是解决您的问题的示例代码。 graph.neighbors(node) 将给出该节点的邻居列表。

 import networkx as nx
import pandas as pd
G = nx.path_graph(5)
Distance=[]
edge0=[]
neighbor0edge0=[]
neighbor1edge1=[]
edge1=[]
Output=pd.DataFrame()
for edge in G.edges():
    neighbor1=[n for n in G.neighbors(edge[0])] #neighborrs w.r.t v1
    neighbor2=[n for n in G.neighbors(edge[1])] #neighborrs w.r.t v2
    distance=[]
    for i in neighbor1:
        for j in neighbor2:
              distance.append(nx.shortest_path_length(G, source=i, target=j)) #Find distance between all the combination of neighbor1 and neighbor2
    edge0.append(edge[0])
    edge1.append(edge[1])
    Distance.append(distance)
    neighbor0edge0.append(neighbor1)
    neighbor1edge1.append(neighbor2)
Output['v1']=edge0
Output['neighborv1']=neighbor0edge0
Output['v2']=edge1
Output['neighborv2']=neighbor1edge1
Output['Distances']=Distance

结果：-

`v1 neighborv1  v2 neighborv2     Distances
 0        [1]   1     [0, 2]        [1, 1]
 1     [0, 2]   2     [1, 3]  [1, 3, 1, 1]
 2     [1, 3]   3     [2, 4]  [1, 3, 1, 1]
 3     [2, 4]   4        [3]        [1, 1]`

Answer 2

你已经很接近了，除了在最后一个循环中你必须再次遍历邻居列表然后存储距离

import numpy as np
import networkx as nx

# Since I didn't have your data, I simply recreated from your R code
graph = nx.Graph()
for i in range(1, 5):
  graph.add_node(i)

for x,y in [(4, 3), (4, 1), (4, 2), (3, 2), (3, 1)]:
  graph.add_edge(x, y)

# print(graph.edges())
# Output EdgeView([(4, 3), (4, 1), (4, 2), (3, 2), (3, 1)])

distance_neighbors = {}

for edge in list(graph.edges):
  neighbor1 = tuple(graph.neighbors(edge[0]))
  neighbor2 = tuple(graph.neighbors(edge[1]))

  distance_list = []
  for v1 in neighbor1:
    for v2 in neighbor2:
      distance_list.append(nx.shortest_path_length(graph, source=v1, target=v2))
  distance_neighbors[edge] = distance_list

distance_neighbours 包含以下数据：

{(1, 3): [0, 1, 1, 1, 1, 1],
 (1, 4): [1, 1, 1, 0, 1, 1],
 (2, 3): [0, 1, 1, 1, 1, 1],
 (2, 4): [1, 1, 1, 0, 1, 1],
 (3, 4): [1, 1, 1, 1, 2, 0, 1, 0, 2]}

最后一条边 (3,4) 中值的排序不同，因为 Python 对邻居的排序与 R 的不同。 为了确保行为相同，请运行以下代码：

import os

import numpy as np
import networkx as nx

# Since I didn't have your data, I simply recreated from your R code
graph = nx.Graph()
for i in range(1, 5):
  graph.add_node(i)

for x,y in [(4, 3), (4, 1), (4, 2), (3, 2), (3, 1)]:
  graph.add_edge(x, y)

# print(graph.edges())
# Output EdgeView([(4, 3), (4, 1), (4, 2), (3, 2), (3, 1)])

distance_neighbors = {}

for edge in list(graph.edges):
  # Just sort the neighbours list in reverse order
  neighbor1 = tuple(sorted(graph.neighbors(edge[0]), reverse=True))
  neighbor2 = tuple(sorted(graph.neighbors(edge[1]), reverse=True))

  distance_list = []
  for v1 in neighbor1:
    for v2 in neighbor2:
      distance_list.append(nx.shortest_path_length(graph, source=v1, target=v2))
  distance_neighbors[edge] = distance_list

现在distance_neighbors 的输出与您的 R 代码相同：

{(1, 3): [0, 1, 1, 1, 1, 1],
 (1, 4): [1, 1, 1, 0, 1, 1],
 (2, 3): [0, 1, 1, 1, 1, 1],
 (2, 4): [1, 1, 1, 0, 1, 1],
 (3, 4): [1, 1, 1, 1, 0, 2, 1, 2, 0]}

这是上面代码的link to the Google Colab notebook。

希望这会有所帮助！