NetworkX Python图网络分析库教程：创建、算法、可视化与复杂网络处理

networkx by davila7/claude-code-templates

231 周安装量

24,300 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill networkx

Python Web框架数据可视化数据分析

🇨🇳中文介绍

NetworkX

概述

NetworkX 是一个用于创建、操作和分析复杂网络与图的 Python 包。当处理网络或图数据结构时，请使用此技能，包括社交网络、生物网络、交通系统、引文网络、知识图谱，或任何涉及实体间关系的系统。

何时使用此技能

当任务涉及以下内容时，请调用此技能：

创建图：从数据构建网络结构，添加带有属性的节点和边
图分析：计算中心性度量、寻找最短路径、检测社区、测量聚类
图算法：运行标准算法，如 Dijkstra、PageRank、最小生成树、最大流
网络生成：创建合成网络（随机、无标度、小世界模型）用于测试或模拟
图输入/输出：从各种格式读取或写入（边列表、GraphML、JSON、CSV、邻接矩阵）
可视化：使用 matplotlib 或交互式库绘制和自定义网络可视化
网络比较：检查同构性、计算图度量、分析结构属性

核心功能

1. 图的创建与操作

NetworkX 支持四种主要的图类型：

Graph：具有单边的无向图
DiGraph：具有单向连接的有向图
MultiGraph：允许节点间存在多条边的无向图
MultiDiGraph：具有多条边的有向图

通过以下方式创建图：

import networkx as nx

# 创建空图
G = nx.Graph()

# 添加节点（可以是任何可哈希类型）
G.add_node(1)
G.add_nodes_from([2, 3, 4])
G.add_node("protein_A", type='enzyme', weight=1.5)

# 添加边
G.add_edge(1, 2)
G.add_edges_from([(1, 3), (2, 4)])
G.add_edge(1, 4, weight=0.8, relation='interacts')

广告位招租

在这里展示您的产品或服务

触达数万 AI 开发者，精准高效

联系我们

相关 Skills

FlyClaw：零登录航班聚合查询工具，Python实现多源航班信息与价格搜索

4,000,000 周安装

Azure Data Explorer (Kusto) 查询技能：KQL数据分析、日志遥测与时间序列处理

138,800 周安装

专业SEO审计工具：全面网站诊断、技术SEO优化与页面分析指南

68,800 周安装

Python PDF处理教程：合并拆分、提取文本表格、创建PDF文件

65,000 周安装

参考：有关创建、修改、检查和图管理结构的全面指导，包括处理属性和子图，请参阅 references/graph-basics.md。

NetworkX 提供了广泛的网络分析算法：

# 查找最短路径
path = nx.shortest_path(G, source=1, target=5)
length = nx.shortest_path_length(G, source=1, target=5, weight='weight')

中心性度量：

# 度中心性
degree_cent = nx.degree_centrality(G)

# 中介中心性
betweenness = nx.betweenness_centrality(G)

# PageRank
pagerank = nx.pagerank(G)

from networkx.algorithms import community

# 检测社区
communities = community.greedy_modularity_communities(G)

# 检查连通性
is_connected = nx.is_connected(G)

# 查找连通分量
components = list(nx.connected_components(G))

参考：有关所有可用算法的详细文档，包括最短路径、中心性度量、聚类、社区检测、流、匹配、树算法和图遍历，请参阅 references/algorithms.md。

为测试、模拟或建模创建合成网络：

# 完全图
G = nx.complete_graph(n=10)

# 环图
G = nx.cycle_graph(n=20)

# 已知图
G = nx.karate_club_graph()
G = nx.petersen_graph()

# Erdős-Rényi 随机图
G = nx.erdos_renyi_graph(n=100, p=0.1, seed=42)

# Barabási-Albert 无标度网络
G = nx.barabasi_albert_graph(n=100, m=3, seed=42)

# Watts-Strogatz 小世界网络
G = nx.watts_strogatz_graph(n=100, k=6, p=0.1, seed=42)

结构化网络：

# 网格图
G = nx.grid_2d_graph(m=5, n=7)

# 随机树
G = nx.random_tree(n=100, seed=42)

参考：有关所有图生成器的全面覆盖，包括经典图、随机模型（Erdős-Rényi、Barabási-Albert、Watts-Strogatz）、格点图、二分图和专用网络模型，以及详细的参数和用例，请参阅 references/generators.md。

NetworkX 支持多种文件格式和数据源：

# 边列表
G = nx.read_edgelist('graph.edgelist')
nx.write_edgelist(G, 'graph.edgelist')

# GraphML（保留属性）
G = nx.read_graphml('graph.graphml')
nx.write_graphml(G, 'graph.graphml')

# GML
G = nx.read_gml('graph.gml')
nx.write_gml(G, 'graph.gml')

# JSON
data = nx.node_link_data(G)
G = nx.node_link_graph(data)

Pandas 集成：

import pandas as pd

# 从 DataFrame
df = pd.DataFrame({'source': [1, 2, 3], 'target': [2, 3, 4], 'weight': [0.5, 1.0, 0.75]})
G = nx.from_pandas_edgelist(df, 'source', 'target', edge_attr='weight')

# 转换为 DataFrame
df = nx.to_pandas_edgelist(G)

import numpy as np

# 邻接矩阵
A = nx.to_numpy_array(G)
G = nx.from_numpy_array(A)

# 稀疏矩阵
A = nx.to_scipy_sparse_array(G)
G = nx.from_scipy_sparse_array(A)

参考：有关所有 I/O 格式的完整文档，包括 CSV、SQL 数据库、Cytoscape、DOT，以及针对不同用例的格式选择指导，请参阅 references/io.md。

创建清晰且信息丰富的网络可视化：

基础可视化：

import matplotlib.pyplot as plt

# 简单绘制
nx.draw(G, with_labels=True)
plt.show()

# 使用布局
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500)
plt.show()

# 按度着色
node_colors = [G.degree(n) for n in G.nodes()]
nx.draw(G, node_color=node_colors, cmap=plt.cm.viridis)

# 按中心性调整大小
centrality = nx.betweenness_centrality(G)
node_sizes = [3000 * centrality[n] for n in G.nodes()]
nx.draw(G, node_size=node_sizes)

# 边权重
edge_widths = [3 * G[u][v].get('weight', 1) for u, v in G.edges()]
nx.draw(G, width=edge_widths)

# Spring 布局（力导向）
pos = nx.spring_layout(G, seed=42)

# 环形布局
pos = nx.circular_layout(G)

# Kamada-Kawai 布局
pos = nx.kamada_kawai_layout(G)

# 谱布局
pos = nx.spectral_layout(G)

plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, node_color='lightblue', node_size=500,
        edge_color='gray', with_labels=True, font_size=10)
plt.title('Network Visualization', fontsize=16)
plt.axis('off')
plt.tight_layout()
plt.savefig('network.png', dpi=300, bbox_inches='tight')
plt.savefig('network.pdf', bbox_inches='tight')  # 矢量格式

参考：有关可视化技术的广泛文档，包括布局算法、自定义选项、使用 Plotly 和 PyVis 的交互式可视化、3D 网络以及创建出版质量图形，请参阅 references/visualization.md。

确保已安装 NetworkX：

# 检查是否已安装
import networkx as nx
print(nx.__version__)

# 如果需要则安装（通过 bash）
# uv pip install networkx
# uv pip install networkx[default]  # 包含可选依赖项

常见工作流程模式

大多数 NetworkX 任务遵循以下模式：

创建或加载图：

# 从头开始
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4)])

# 或从文件/数据加载
G = nx.read_edgelist('data.txt')

检查结构：

print(f"Nodes: {G.number_of_nodes()}")
print(f"Edges: {G.number_of_edges()}")
print(f"Density: {nx.density(G)}")
print(f"Connected: {nx.is_connected(G)}")

分析：

# 计算度量
degree_cent = nx.degree_centrality(G)
avg_clustering = nx.average_clustering(G)

# 查找路径
path = nx.shortest_path(G, source=1, target=4)

# 检测社区
communities = community.greedy_modularity_communities(G)

可视化：

pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, with_labels=True)
plt.show()

导出结果：

# 保存图
nx.write_graphml(G, 'analyzed_network.graphml')

# 保存度量
df = pd.DataFrame({
    'node': list(degree_cent.keys()),
    'centrality': list(degree_cent.values())
})
df.to_csv('centrality_results.csv', index=False)

浮点精度：当图中包含浮点数时，由于精度限制，所有结果本质上都是近似的。这可能会影响算法结果，特别是在最小/最大计算中。

内存与性能：每次脚本运行时，图数据都必须加载到内存中。对于大型网络：

使用适当的数据结构（大型稀疏图使用稀疏矩阵）
考虑仅加载必要的子图
使用高效的文件格式（Python 对象使用 pickle，压缩格式）
对于非常大的网络，利用近似算法（例如，中心性计算中的 k 参数）

节点和边类型：

节点可以是任何可哈希的 Python 对象（数字、字符串、元组、自定义对象）
为清晰起见，请使用有意义的标识符
删除节点时，所有关联的边会自动删除

随机种子：在随机图生成和力导向布局中，始终设置随机种子以确保可重现性：

G = nx.erdos_renyi_graph(n=100, p=0.1, seed=42)
pos = nx.spring_layout(G, seed=42)

# 创建
G = nx.Graph()
G.add_edge(1, 2)

# 查询
G.number_of_nodes()
G.number_of_edges()
G.degree(1)
list(G.neighbors(1))

# 检查
G.has_node(1)
G.has_edge(1, 2)
nx.is_connected(G)

# 修改
G.remove_node(1)
G.remove_edge(1, 2)
G.clear()

# 路径
nx.shortest_path(G, source, target)
nx.all_pairs_shortest_path(G)

# 中心性
nx.degree_centrality(G)
nx.betweenness_centrality(G)
nx.closeness_centrality(G)
nx.pagerank(G)

# 聚类
nx.clustering(G)
nx.average_clustering(G)

# 分量
nx.connected_components(G)
nx.strongly_connected_components(G)  # 有向图

# 社区
community.greedy_modularity_communities(G)

文件 I/O 快速参考

# 读取
nx.read_edgelist('file.txt')
nx.read_graphml('file.graphml')
nx.read_gml('file.gml')

# 写入
nx.write_edgelist(G, 'file.txt')
nx.write_graphml(G, 'file.graphml')
nx.write_gml(G, 'file.gml')

# Pandas
nx.from_pandas_edgelist(df, 'source', 'target')
nx.to_pandas_edgelist(G)

此技能包含全面的参考文档：

references/graph-basics.md

关于图类型、创建和修改图、添加节点和边、管理属性、检查结构以及处理子图的详细指南。

references/algorithms.md

NetworkX 算法的完整覆盖，包括最短路径、中心性度量、连通性、聚类、社区检测、流算法、树算法、匹配、着色、同构和图遍历。

references/generators.md

图生成器的全面文档，包括经典图、随机模型（Erdős-Rényi、Barabási-Albert、Watts-Strogatz）、格点图、树、社交网络模型和专用生成器。

以各种格式读写图的完整指南：边列表、邻接列表、GraphML、GML、JSON、CSV、Pandas DataFrames、NumPy 数组、SciPy 稀疏矩阵、数据库集成和格式选择指南。

references/visualization.md

关于可视化技术的广泛文档，包括布局算法、自定义节点和边外观、标签、使用 Plotly 和 PyVis 的交互式可视化、3D 网络、二分图布局以及创建出版质量图形。

🇺🇸English

NetworkX

Overview

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities.

When to Use This Skill

Invoke this skill when tasks involve:

Creating graphs : Building network structures from data, adding nodes and edges with attributes
Graph analysis : Computing centrality measures, finding shortest paths, detecting communities, measuring clustering
Graph algorithms : Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow
Network generation : Creating synthetic networks (random, scale-free, small-world models) for testing or simulation
Graph I/O : Reading from or writing to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices)
Visualization : Drawing and customizing network visualizations with matplotlib or interactive libraries
Network comparison : Checking isomorphism, computing graph metrics, analyzing structural properties

Core Capabilities

1. Graph Creation and Manipulation

NetworkX supports four main graph types:

Graph : Undirected graphs with single edges
DiGraph : Directed graphs with one-way connections
MultiGraph : Undirected graphs allowing multiple edges between nodes
MultiDiGraph : Directed graphs with multiple edges

Create graphs by:

import networkx as nx

# Create empty graph
G = nx.Graph()

# Add nodes (can be any hashable type)
G.add_node(1)
G.add_nodes_from([2, 3, 4])
G.add_node("protein_A", type='enzyme', weight=1.5)

# Add edges
G.add_edge(1, 2)
G.add_edges_from([(1, 3), (2, 4)])
G.add_edge(1, 4, weight=0.8, relation='interacts')

Reference : See references/graph-basics.md for comprehensive guidance on creating, modifying, examining, and managing graph structures, including working with attributes and subgraphs.

2. Graph Algorithms

NetworkX provides extensive algorithms for network analysis:

Shortest Paths :

# Find shortest path
path = nx.shortest_path(G, source=1, target=5)
length = nx.shortest_path_length(G, source=1, target=5, weight='weight')

Centrality Measures :

# Degree centrality
degree_cent = nx.degree_centrality(G)

# Betweenness centrality
betweenness = nx.betweenness_centrality(G)

# PageRank
pagerank = nx.pagerank(G)

Community Detection :

from networkx.algorithms import community

# Detect communities
communities = community.greedy_modularity_communities(G)

Connectivity :

# Check connectivity
is_connected = nx.is_connected(G)

# Find connected components
components = list(nx.connected_components(G))

Reference : See references/algorithms.md for detailed documentation on all available algorithms including shortest paths, centrality measures, clustering, community detection, flows, matching, tree algorithms, and graph traversal.

3. Graph Generators

Create synthetic networks for testing, simulation, or modeling:

Classic Graphs :

# Complete graph
G = nx.complete_graph(n=10)

# Cycle graph
G = nx.cycle_graph(n=20)

# Known graphs
G = nx.karate_club_graph()
G = nx.petersen_graph()

Random Networks :

# Erdős-Rényi random graph
G = nx.erdos_renyi_graph(n=100, p=0.1, seed=42)

# Barabási-Albert scale-free network
G = nx.barabasi_albert_graph(n=100, m=3, seed=42)

# Watts-Strogatz small-world network
G = nx.watts_strogatz_graph(n=100, k=6, p=0.1, seed=42)

Structured Networks :

# Grid graph
G = nx.grid_2d_graph(m=5, n=7)

# Random tree
G = nx.random_tree(n=100, seed=42)

Reference : See references/generators.md for comprehensive coverage of all graph generators including classic, random, lattice, bipartite, and specialized network models with detailed parameters and use cases.

4. Reading and Writing Graphs

NetworkX supports numerous file formats and data sources:

File Formats :

# Edge list
G = nx.read_edgelist('graph.edgelist')
nx.write_edgelist(G, 'graph.edgelist')

# GraphML (preserves attributes)
G = nx.read_graphml('graph.graphml')
nx.write_graphml(G, 'graph.graphml')

# GML
G = nx.read_gml('graph.gml')
nx.write_gml(G, 'graph.gml')

# JSON
data = nx.node_link_data(G)
G = nx.node_link_graph(data)

Pandas Integration :

import pandas as pd

# From DataFrame
df = pd.DataFrame({'source': [1, 2, 3], 'target': [2, 3, 4], 'weight': [0.5, 1.0, 0.75]})
G = nx.from_pandas_edgelist(df, 'source', 'target', edge_attr='weight')

# To DataFrame
df = nx.to_pandas_edgelist(G)

Matrix Formats :

import numpy as np

# Adjacency matrix
A = nx.to_numpy_array(G)
G = nx.from_numpy_array(A)

# Sparse matrix
A = nx.to_scipy_sparse_array(G)
G = nx.from_scipy_sparse_array(A)

Reference : See references/io.md for complete documentation on all I/O formats including CSV, SQL databases, Cytoscape, DOT, and guidance on format selection for different use cases.

5. Visualization

Create clear and informative network visualizations:

Basic Visualization :

import matplotlib.pyplot as plt

# Simple draw
nx.draw(G, with_labels=True)
plt.show()

# With layout
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500)
plt.show()

Customization :

# Color by degree
node_colors = [G.degree(n) for n in G.nodes()]
nx.draw(G, node_color=node_colors, cmap=plt.cm.viridis)

# Size by centrality
centrality = nx.betweenness_centrality(G)
node_sizes = [3000 * centrality[n] for n in G.nodes()]
nx.draw(G, node_size=node_sizes)

# Edge weights
edge_widths = [3 * G[u][v].get('weight', 1) for u, v in G.edges()]
nx.draw(G, width=edge_widths)

Layout Algorithms :

# Spring layout (force-directed)
pos = nx.spring_layout(G, seed=42)

# Circular layout
pos = nx.circular_layout(G)

# Kamada-Kawai layout
pos = nx.kamada_kawai_layout(G)

# Spectral layout
pos = nx.spectral_layout(G)

Publication Quality :

plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, node_color='lightblue', node_size=500,
        edge_color='gray', with_labels=True, font_size=10)
plt.title('Network Visualization', fontsize=16)
plt.axis('off')
plt.tight_layout()
plt.savefig('network.png', dpi=300, bbox_inches='tight')
plt.savefig('network.pdf', bbox_inches='tight')  # Vector format

Reference : See references/visualization.md for extensive documentation on visualization techniques including layout algorithms, customization options, interactive visualizations with Plotly and PyVis, 3D networks, and publication-quality figure creation.

Working with NetworkX

Installation

Ensure NetworkX is installed:

# Check if installed
import networkx as nx
print(nx.__version__)

# Install if needed (via bash)
# uv pip install networkx
# uv pip install networkx[default]  # With optional dependencies

Common Workflow Pattern

Most NetworkX tasks follow this pattern:

Create or Load Graph :

# From scratch
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4)])

# Or load from file/data
G = nx.read_edgelist('data.txt')

Examine Structure :

print(f"Nodes: {G.number_of_nodes()}")
print(f"Edges: {G.number_of_edges()}")
print(f"Density: {nx.density(G)}")
print(f"Connected: {nx.is_connected(G)}")

Analyze :

# Compute metrics
degree_cent = nx.degree_centrality(G)
avg_clustering = nx.average_clustering(G)

# Find paths
path = nx.shortest_path(G, source=1, target=4)

# Detect communities
communities = community.greedy_modularity_communities(G)

Visualize :

pos = nx.spring_layout(G, seed=42)
nx.draw(G, pos=pos, with_labels=True)
plt.show()

Export Results :

Important Considerations

Floating Point Precision : When graphs contain floating-point numbers, all results are inherently approximate due to precision limitations. This can affect algorithm outcomes, particularly in minimum/maximum computations.

Memory and Performance : Each time a script runs, graph data must be loaded into memory. For large networks:

Use appropriate data structures (sparse matrices for large sparse graphs)
Consider loading only necessary subgraphs
Use efficient file formats (pickle for Python objects, compressed formats)
Leverage approximate algorithms for very large networks (e.g., k parameter in centrality calculations)

Node and Edge Types :

Nodes can be any hashable Python object (numbers, strings, tuples, custom objects)
Use meaningful identifiers for clarity
When removing nodes, all incident edges are automatically removed

Random Seeds : Always set random seeds for reproducibility in random graph generation and force-directed layouts:

G = nx.erdos_renyi_graph(n=100, p=0.1, seed=42)
pos = nx.spring_layout(G, seed=42)

Quick Reference

Basic Operations

# Create
G = nx.Graph()
G.add_edge(1, 2)

# Query
G.number_of_nodes()
G.number_of_edges()
G.degree(1)
list(G.neighbors(1))

# Check
G.has_node(1)
G.has_edge(1, 2)
nx.is_connected(G)

# Modify
G.remove_node(1)
G.remove_edge(1, 2)
G.clear()

Essential Algorithms

# Paths
nx.shortest_path(G, source, target)
nx.all_pairs_shortest_path(G)

# Centrality
nx.degree_centrality(G)
nx.betweenness_centrality(G)
nx.closeness_centrality(G)
nx.pagerank(G)

# Clustering
nx.clustering(G)
nx.average_clustering(G)

# Components
nx.connected_components(G)
nx.strongly_connected_components(G)  # Directed

# Community
community.greedy_modularity_communities(G)

File I/O Quick Reference

# Read
nx.read_edgelist('file.txt')
nx.read_graphml('file.graphml')
nx.read_gml('file.gml')

# Write
nx.write_edgelist(G, 'file.txt')
nx.write_graphml(G, 'file.graphml')
nx.write_gml(G, 'file.gml')

# Pandas
nx.from_pandas_edgelist(df, 'source', 'target')
nx.to_pandas_edgelist(G)

Resources

This skill includes comprehensive reference documentation:

references/graph-basics.md

Detailed guide on graph types, creating and modifying graphs, adding nodes and edges, managing attributes, examining structure, and working with subgraphs.

references/algorithms.md

Complete coverage of NetworkX algorithms including shortest paths, centrality measures, connectivity, clustering, community detection, flow algorithms, tree algorithms, matching, coloring, isomorphism, and graph traversal.

references/generators.md

Comprehensive documentation on graph generators including classic graphs, random models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz), lattices, trees, social network models, and specialized generators.

references/io.md

Complete guide to reading and writing graphs in various formats: edge lists, adjacency lists, GraphML, GML, JSON, CSV, Pandas DataFrames, NumPy arrays, SciPy sparse matrices, database integration, and format selection guidelines.

references/visualization.md

Extensive documentation on visualization techniques including layout algorithms, customizing node and edge appearance, labels, interactive visualizations with Plotly and PyVis, 3D networks, bipartite layouts, and creating publication-quality figures.

Additional Resources

Official Documentation : https://networkx.org/documentation/latest/
Tutorial : https://networkx.org/documentation/latest/tutorial.html
Gallery : https://networkx.org/documentation/latest/auto_examples/index.html
GitHub : https://github.com/networkx/networkx

Weekly Installs

151

Repository

davila7/claude-…emplates

GitHub Stars

22.6K

First Seen

Jan 21, 2026

Security Audits

Gen Agent Trust HubPass SocketPass SnykPass

Installed on

claude-code122

opencode120

gemini-cli114

cursor112

codex102

antigravity99

DOCX文件创建、编辑与分析完整指南 - 使用docx-js、Pandoc和Python脚本

51,800 周安装

# Save graph
nx.write_graphml(G, 'analyzed_network.graphml')

# Save metrics
df = pd.DataFrame({
    'node': list(degree_cent.keys()),
    'centrality': list(degree_cent.values())
})
df.to_csv('centrality_results.csv', index=False)

NetworkX Python图网络分析库教程：创建、算法、可视化与复杂网络处理

🇨🇳中文介绍

NetworkX

概述

何时使用此技能

核心功能

1. 图的创建与操作

相关 Skills

2. 图算法

3. 图生成器

4. 读写图

5. 可视化

使用 NetworkX

安装

常见工作流程模式

重要注意事项

快速参考

基本操作

基本算法

文件 I/O 快速参考

资源

references/graph-basics.md

references/algorithms.md

references/generators.md

references/io.md

references/visualization.md

其他资源

🇺🇸English

NetworkX

Overview

When to Use This Skill

Core Capabilities

1. Graph Creation and Manipulation

2. Graph Algorithms

3. Graph Generators

4. Reading and Writing Graphs

5. Visualization

Working with NetworkX

Installation

Common Workflow Pattern

Important Considerations

Quick Reference

Basic Operations

Essential Algorithms

File I/O Quick Reference

Resources

references/graph-basics.md

references/algorithms.md

references/generators.md

references/io.md

references/visualization.md

Additional Resources

最新 Skills