networkx

Name: networkx
Availability: InStock
Author: rubensliv

Analyze and visualize complex networks and graphs in Python.

15|2|Updated Dec 17, 2025

Search Tags:#analysis #visualization #graph #networkx #centrality #shortest-path #networks

Ready to run

npx skills add https://github.com/rubensliv/k-dense-ai --skill networkx-rubensliv

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/networkx
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill networkx-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Analyze and visualize complex networks and graphs in Python.

Core Features & Use Cases

Create and manipulate Graph, DiGraph, MultiGraph, and MultiDiGraph objects with attributes.
Run graph algorithms (shortest paths, centrality, clustering) and generate synthetic networks.
Visualize networks and import/export across common formats for analysis and research.

Quick Start

Import networkx as nx, build a small graph, and compute a basic metric to get started.

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?▼

To analyze and visualize complex networks in Python, you can build graphs, run algorithms for shortest paths and centrality, detect communities, and render layouts across social, biological, and transportation networks.

What graph types are supported for network analysis?▼

Network analysis supports Graph, DiGraph, MultiGraph, and MultiDiGraph object types, allowing you to create and manipulate complex network structures with custom attributes.

Can I compute shortest paths and centrality metrics for social networks?▼

Yes, you can compute shortest paths and centrality metrics for social networks by running built-in graph algorithms designed to analyze relationships and node importance across various network structures.

What's the best way to generate synthetic networks for research?▼

The best way to generate synthetic networks for research is using built-in network generators that create synthetic graphs for testing algorithms and modeling structural properties across knowledge and transportation networks.

Does this approach work for importing and exporting common graph formats?▼

Yes, this approach works for importing and exporting common graph formats, providing I/O utilities that allow you to load existing network data and save analysis results for further research.

When do I need specialized graph algorithms for biological networks?▼

You need specialized graph algorithms for biological networks when analyzing complex relationships, requiring clustering, centrality measures, and shortest path computations to understand structural connectivity and node significance.