networkx

Create, analyze, and visualize graphs with Python NetworkX.

9|2|Updated Mar 12, 2026

Search Tags:#visualization #data-analysis #graphs #networkx #centrality #shortest-path #graph-algorithms

Ready to run

npx skills add https://github.com/hongmaple0820/agent-academy --skill networkx-hongmaple0820

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/hongmaple0820/agent-academy/tree/main/skills/ai-ml/networkx
Command: npx skills add https://github.com/hongmaple0820/agent-academy --skill networkx-hongmaple0820

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Graph analysis and visualization often require a flexible toolkit; NetworkX provides a Python library to create, manipulate, analyze, and visualize networks with a wide range of algorithms, I/O utilities, and layout options.

Core Features & Use Cases

Graph creation and manipulation: support for Graph, DiGraph, MultiGraph types and attribute-rich nodes/edges.
Graph algorithms: shortest paths, centrality measures, clustering, community detection, and more.
I/O and interoperability: read/write GraphML, GML, JSON, adjacency lists, edge lists, and integrate with Pandas.
Visualization and layout: layout algorithms and plotting with Matplotlib or interactive tools.
Domain scopes: social networks, biological networks, transportation, knowledge graphs.

Quick Start

Install NetworkX and create a simple graph, then compute the shortest path between two nodes.

Frequently Asked Questions about networkx

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze and visualize graphs using Python?▼

You can build, analyze, and visualize graphs in Python by creating Graph or DiGraph objects, applying algorithms like shortest paths, and plotting the results with Matplotlib.

What Python library can I use to find the shortest path in a transportation network?▼

Python graph analysis supports computing the shortest path between nodes in transportation networks using built-in shortest path algorithms on directed or undirected graph structures.

Does NetworkX support reading and exporting GraphML and JSON formats?▼

Graph data I/O supports reading and writing multiple formats including GraphML, GML, JSON, adjacency lists, and edge lists, allowing seamless import and export of network data.

How do I calculate centrality measures and detect communities in social network data?▼

Centrality measures and community detection are computed by loading social network nodes and edges into a graph object, then applying the respective graph algorithms to identify influential nodes and clusters.

Can I use Pandas with NetworkX to analyze biological networks?▼

Graph analysis integrates with Pandas, enabling you to load biological network data from DataFrames into graph structures for subsequent algorithmic analysis and visualization.

What is the difference between Graph, DiGraph, and MultiGraph types for network analysis?▼

Graph supports undirected edges, DiGraph handles directed relationships, and MultiGraph allows multiple parallel edges between nodes, accommodating diverse network modeling requirements.