Python/NetworkX：动态计算边权重

Question

我有一个使用networkx 创建的未加权 图，我想根据边出现的计数/频率计算节点之间边的权重。我的图表中的一条边可以出现不止一次，但边出现的频率是事先不知道的。目的是根据连接节点之间移动的权重（例如计数/频率）来可视化边缘。本质上，我想创建一个连接节点之间移动的网络流量图，并根据颜色或边缘宽度进行可视化。例如，从节点 0 到 1 的边之间有 10 次移动，而节点 1 到 2 有 5 次移动，因此边 0-1 将使用不同的边颜色/大小进行可视化。

如何动态计算两个节点之间的边的权重（在使用g.add_edges_from() 将它们添加到图表之后），然后重新应用到我的图表以进行可视化？下面是我最初用于创建图表的图表、数据和代码的示例，以及我尝试失败的解决方案。

图表

样本数据

簇质心（节点）

cluster_label,latitude,longitude
0,39.18193382,-77.51885109
1,39.18,-77.27
2,39.17917928,-76.6688633
3,39.1782,-77.2617
4,39.1765,-77.1927
5,39.1762375,-76.8675441
6,39.17468,-76.8204499
7,39.17457332,-77.2807235
8,39.17406072,-77.274685
9,39.1731621,-77.2716502
10,39.17,-77.27

轨迹（边）

user_id,trajectory
11011.0,"[[340, 269], [269, 340]]"
80973.0,"[[398, 279]]"
608473.0,"[[69, 28]]"
2139671.0,"[[382, 27], [27, 285]]"
3945641.0,"[[120, 422], [422, 217], [217, 340], [340, 340]]"
5820642.0,"[[458, 442]]"
6060732.0,"[[291, 431]]"
6912362.0,"[[68, 27]]"
7362602.0,"[[112, 269]]"
8488782.0,"[[133, 340], [340, 340]]"

代码

import csv
import networkx as nx
import pandas as pd
import community
import matplotlib.pyplot as plt
import time
import mplleaflet

g = nx.MultiGraph()

df = pd.read_csv('cluster_centroids.csv', delimiter=',')
df['pos'] = list(zip(df.longitude,df.latitude))
dict_pos = dict(zip(df.cluster_label,df.pos))
#print dict_pos

for row in csv.reader(open('edges.csv', 'r')):
    if '[' in row[1]:       #
        g.add_edges_from(eval(row[1]))

# Plotting with mplleaflet
fig, ax = plt.subplots()
nx.draw_networkx_nodes(g,pos=dict_pos,node_size=50,node_color='b')
nx.draw_networkx_edges(g,pos=dict_pos,linewidths=0.01,edge_color='k', alpha=.05)
nx.draw_networkx_labels(g,dict_pos)
mplleaflet.show(fig=ax.figure)

我尝试使用g.add_weighted_edges_from() 并添加weight=1 作为属性，但没有任何运气。我也试过用这个也没有用：

for u,v,d in g.edges():
    d['weight'] = 1
g.edges(data=True)
edges = g.edges()
weights = [g[u][v]['weight'] for u,v in edges]

Answer 1

由于没有得到答复，因此打开了关于此主题的第二个问题（此处：Python/NetworkX: Add Weights to Edges by Frequency of Edge Occurance）并收到了回复。根据边缘出现的计数为边缘添加权重：

g = nx.MultiDiGraph()

df = pd.read_csv('G:\cluster_centroids.csv', delimiter=',')
df['pos'] = list(zip(df.longitude,df.latitude))
dict_pos = dict(zip(df.cluster_label,df.pos))
#print dict_pos


for row in csv.reader(open('G:\edges.csv', 'r')):
    if '[' in row[1]:       #
        g.add_edges_from(eval(row[1]))

for u, v, d in g.edges(data=True):
    d['weight'] = 1
for u,v,d in g.edges(data=True):
    print u,v,d

根据上述计数缩放颜色和边缘宽度：

minLineWidth = 0.25

for u, v, d in g.edges(data=True):
    d['weight'] = c[u, v]*minLineWidth
edges,weights = zip(*nx.get_edge_attributes(g,'weight').items())

values = range(len(g.edges()) 
jet = cm = plt.get_cmap('YlOrRd')
cNorm  = colors.Normalize(vmin=0, vmax=values[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
colorList = []

for i in range(len(g.edges()):
    colorVal = scalarMap.to_rgba(values[i])
    colorList.append(colorVal)

并将width=[d['weight'] for u,v, d in g.edges(data=True)]、edge_color=colorList 作为nx.draw_networkx_edges() 中的参数传递