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networkx

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

321|26|Updated Mar 10, 2026
Search Tags:#python#algorithms#visualization#graphs#io#networkx#network-analysis
One-click install
npx skills add https://github.com/mkurman/tamux --skill networkx-mkurman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/networkx
Command: npx skills add https://github.com/mkurman/tamux --skill networkx-mkurman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NetworkX provides a Python toolkit for creating, analyzing, and visualizing complex networks and graphs, enabling researchers and developers to study relationships and structure in data.

Core Features & Use Cases

  • Graph creation and manipulation with Graph, DiGraph, MultiGraph, and MultiDiGraph
  • Graph algorithms including shortest paths, centrality, clustering, and community detection
  • Data I/O across formats (GraphML, GML, JSON, CSV, adjacency, edge lists) and publication-ready visualizations
  • Use cases span social networks, biological networks, transportation systems, citation networks, and knowledge graphs

Quick Start

Install NetworkX and run a quick example to create a simple graph and 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 complex networks in Python?▼

You can analyze and visualize complex networks in Python by creating graph structures, applying algorithms like shortest paths and centrality, and rendering publication-ready visualizations from the computed node and edge relationships.

What Python graph algorithms are available for community detection and centrality?▼

Available Python graph algorithms include shortest paths, centrality measures, clustering, and community detection, enabling structural analysis of relationships across social, biological, and transportation networks.

Can I import and export graphs using JSON, CSV, and GraphML formats?▼

Yes, you can import and export graphs using JSON, CSV, GraphML, GML, adjacency, and edge list formats, enabling seamless integration with existing data pipelines and external tools.

Does this network analysis approach work for biological networks and knowledge graphs?▼

Yes, this network analysis approach works for biological networks and knowledge graphs, as well as social networks, transportation systems, and citation networks, handling any domain involving relationship structures.

What is the best way to model directed and undirected graphs in Python?▼

The best way to model directed and undirected graphs in Python is using dedicated classes like Graph for undirected structures and DiGraph for directed relationships, with MultiGraph and MultiDiGraph supporting parallel edges.

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