Python / NetworkX：动态计算边缘权重

Question

我有一个用<{1}}创建的未加权图表，我想根据边缘出现的计数/频率计算节点之间边缘的权重。我的图形中的边缘可能出现不止一次，但边缘外观的频率事先不知道。目的是基于连接节点之间的移动的权重（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的边缘在它们之间有10个移动，节点1到2有5个，因此边缘0-1将使用不同的边缘颜色/大小可视化。

如何动态计算两个节点之间边缘的权重（在使用networkx将其添加到图形之后），然后重新应用到我的图形以进行可视化？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。

图形

示例数据

群集质心（节点）

g.add_edges_from()

轨迹（边缘）

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

代码

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]]"

我尝试使用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) 并添加g.add_weighted_edges_from()作为属性，但没有任何运气。我也试过使用它也没用：

weight=1

Answer 1

由于没有得到答复，关于这个主题的第二个问题被打开了（这里：Python/NetworkX: Add Weights to Edges by Frequency of Edge Occurance），收到了答复。根据边缘出现次数向边添加权重：

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

根据以上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