networkx

Create, analyze, and visualize complex networks and graphs in Python.

8|Updated Jan 13, 2026

Search Tags:#python #algorithms #visualization #data structures #network analysis #graph

Ready to run

npx skills add https://github.com/hxk622/TokenDance --skill networkx-hxk622

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/visualization/networkx
Command: npx skills add https://github.com/hxk622/TokenDance --skill networkx-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a powerful toolkit for creating, analyzing, and visualizing complex networks and graphs, transforming abstract relationships into understandable insights.

Core Features & Use Cases

Graph Creation & Manipulation: Build and modify network structures from data.
Advanced Analysis: Compute centrality, shortest paths, community structures, and more.
Visualization: Generate clear and informative network diagrams.
Use Case: Analyze a social network to identify key influencers, map out protein interactions in biology, or visualize traffic flow in a transportation system.

Quick Start

Use the networkx skill to draw a spring layout of the provided graph data.

Frequently Asked Questions about networkx

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

FAQPage Schema

How do I analyze a social network to identify key influencers in Python?▼

Network analysis in Python involves creating graph data structures from your relational data and computing centrality measures. This toolkit enables you to calculate metrics like degree or betweenness centrality to pinpoint key influencers within complex social networks.

What is the best way to visualize complex network topologies and graph data?▼

Visualizing complex network topologies is achieved by generating layout diagrams from graph data structures. This skill provides visualization functions to map out network structures, such as drawing a spring layout to represent pairwise relationships clearly.

Can I compute shortest paths and detect communities in biological networks using Python?▼

Yes, you can compute shortest paths and detect community structures in biological networks using Python. This toolkit provides advanced graph algorithms specifically designed to analyze protein interactions and other pairwise biological relationships.

Does this Python toolkit support graph creation from custom data structures for transportation systems?▼

Yes, this Python toolkit supports graph creation from custom data structures for transportation systems. You can build and manipulate network structures to map out traffic flow and compute routes using the available graph algorithms.

What are the limitations of using standard graph algorithms for large-scale network analysis?▼

Limitations of graph algorithms for large-scale network analysis depend on the complexity of the data structures and the specific centrality or shortest path computations required. While designed for complex networks, performance may vary based on graph size.