python - 使用 python3 表示实体之间的关系

Question

我想使用 Python3 的 networkx 库来表示一系列数据，但我不知道如何解决这个问题。

基本上我在两个实体之间有一个关系，它在一个名为nations.csv 的csv 文件中。它看起来像这样:

China, Economicaid, Egypt
China, Economicaid, Indonesia
USSR, Economicaid, Cuba 
USSR, Economicaid, India
USSR, Economicaid, Poland
UK, Economicaid, India 
UK, Economicaid, Jordan
USA, Economicaid, Brazil

了解第一行是与第二行之间的第三行(实体 2)相关的实体之一:

我已经解析了 csv 文件，以便将每一行存储在字典中，如下所示:

d = {}
d['entity1'] = []
d['relation'] = []
d['entity2'] = []

dictReader = csv.DictReader(open('nations.csv', 'rt'), fieldnames = 
['entity1', 'relation', 'entity2'], delimiter = ',', quotechar = '"')

for row in dictReader:
    for key in row:
        d[key].append(row[key])

我设法做的是使用函数 add_node() 绘制节点，如下例所示:

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


d = {}
d['entity1'] = []
d['relation'] = []
d['entity2'] = []

dictReader = csv.DictReader(open('nations.csv', 'rt'), fieldnames = ['entity1', 'relation', 'entity2'], delimiter = ',', quotechar = '"')

for row in dictReader:
    for key in row:
        d[key].append(row[key])

print()

for i in range (1, len(d['entity1'])):
    r.append(d['entity1'][i])

for k in range (1, len(d['entity2'])):
    o.append(d['entity2'][k])


G=nx.Graph()

for j in range(len(r)):
    G.add_node(r[j])
    G.add_node(o[j])

nx.draw_networkx(G, with_labels = True, node_size = 500)

plt.show()

但是当我想表示节点之间的边时，问题就来了，因为它不仅是边本身，它还有自己的标签和含义。

Answer 1

基本上，标志 with_labels 应该真正称为 with_node_labels 因为它只触发节点标签的绘制。因此，您需要在绘制图形 (networkx.draw`) 后手动添加边标签 (networkx.draw_networkx_edge_labels)，并且您需要 1) 在同一轴上绘制，以及 2) 使用两个图的图形布局相同。

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import csv

# load data
dictReader = csv.DictReader(open('nations.csv', 'rt'), fieldnames = ['entity1', 'relation', 'entity2'], delimiter = ',', quotechar = '"')

# create a more amenable data structure
edge_to_label = {(d['entity1'], d['entity2']) : d['relation'] for d in dictReader}

# create graph
G = nx.from_edgelist(edge_to_label.keys())

# precompute layout (default layout used here)
layout = nx.layout.fruchterman_reingold_layout(G)

# create figure so we plot the graph and labels on the same plot
fig, ax = plt.subplots(1,1)

# draw graph
nx.draw(G, pos=layout, ax=ax, with_labels=True)

# draw labels using the same, pre-computed graph layout
nx.draw_networkx_edge_labels(G, pos=layout, edge_labels=edge_to_label, ax=ax)

plt.show()