Networkx：使用位置提升节点上方的标签

Question

我对networkx很陌生（今天才开始！）： 我正在使用这两个链接并进行复制：

This is for creating the network

和This is for how I tried adjusting the label positions

所以我的看起来像这样：

layout = nx.spring_layout(g,k=0.2,iterations=50)
for l in layout:  # raise text positions
    layout[l][1] += 0.5

我将 0.5 调整为更小甚至更大的值，但是没有任何反应，没有调整。完全没有变化。我做错了什么？

两个代码组合起来是这样的：

import matplotlib.pyplot as plt
import pandas as pd
import networkx as nx
import os

plt.figure(figsize=(12, 12))

df = pd.read_csv(df_path)

# 1. Create the graph
g = nx.from_pandas_edgelist(df, source='name', target='club') 
# 2. Create a layout for our nodes 
layout = nx.spring_layout(g,k=0.2,iterations=50)
for l in layout:  # raise text positions
    layout[l][1] += 0.5
# 3. Draw the parts we want
nx.draw_networkx_edges(g, layout, edge_color='#AAAAAA')

clubs = [node for node in g.nodes() if node in df.club.unique()]
size = [g.degree(node) * 80 for node in g.nodes() if node in df.club.unique()]
nx.draw_networkx_nodes(g, layout, nodelist=clubs, node_size=size, node_color='lightblue')

people = [node for node in g.nodes() if node in df.name.unique()]
nx.draw_networkx_nodes(g, layout, nodelist=people, node_size=100, node_color='#AAAAAA')

high_degree_people = [node for node in g.nodes() if node in df.name.unique() and g.degree(node) > 1]
nx.draw_networkx_nodes(g, layout, nodelist=high_degree_people, node_size=100, node_color='#fc8d62')

club_dict = dict(zip(clubs, clubs))
nx.draw_networkx_labels(g, layout, labels=club_dict)

# 4. Turn off the axis because I know you don't want it
plt.axis('off')

plt.title("Revolutionary Clubs")

非常感谢您！

顺便说一句，有人有关于networkx的精彩教程吗？我一直在谷歌搜索，但没有找到太多。如果您知道 networkx 教程，这些教程展示了如何构建交互式网络，那就更好了！

Answer 1

您首先需要绘制图形，然后添加值（或为标签的位置创建第二个变量）。 如果您再次阅读code for positioning the labels，您会看到他们首先绘制图形，然后修改布局并绘制标签。

您的代码只是移动所有内容，即沿 y 轴移动标签和边缘。 我已在您的代码中更正了调整的位置：

import matplotlib.pyplot as plt
import pandas as pd
import networkx as nx
import os

plt.figure(figsize=(12, 12))

df = pd.read_csv(df_path)

# 1. Create the graph
g = nx.from_pandas_edgelist(df, source='name', target='club') 
# 2. Create a layout for our nodes 
layout = nx.spring_layout(g,k=0.2,iterations=50)
#
# ----- removed correction
#

# 3. Draw the parts we want
nx.draw_networkx_edges(g, layout, edge_color='#AAAAAA')

clubs = [node for node in g.nodes() if node in df.club.unique()]
size = [g.degree(node) * 80 for node in g.nodes() if node in df.club.unique()]
nx.draw_networkx_nodes(g, layout, nodelist=clubs, node_size=size, node_color='lightblue')

people = [node for node in g.nodes() if node in df.name.unique()]
nx.draw_networkx_nodes(g, layout, nodelist=people, node_size=100, node_color='#AAAAAA')

high_degree_people = [node for node in g.nodes() if node in df.name.unique() and g.degree(node) > 1]
nx.draw_networkx_nodes(g, layout, nodelist=high_degree_people, node_size=100, node_color='#fc8d62')

club_dict = dict(zip(clubs, clubs))

# ------> and move it here
for l in layout:  # raise text positions
    layout[l][1] += 0.1  # probably small value enough
nx.draw_networkx_labels(g, layout, labels=club_dict)

# 4. Turn off the axis because I know you don't want it
plt.axis('off')

plt.title("Revolutionary Clubs")