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networkx

Create, analyze, and visualize graphs and networks in Python with NetworkX.

1|Updated Mar 11, 2026
Search Tags:#python#visualization#graphs#networkx#centrality#networks
One-click install
npx skills add https://github.com/SciMate-AI/scicli --skill networkx-scimate-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/networkx
Command: npx skills add https://github.com/SciMate-AI/scicli --skill networkx-scimate-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NetworkX provides a robust library for creating, manipulating, analyzing, and visualizing graphs and networks in Python.

Core Features & Use Cases

  • Graph creation and manipulation for undirected and directed graphs, including MultiGraph and MultiDiGraph support.
  • Centrality measures, shortest paths, clustering, connectivity, and community detection, IO, and visualization utilities.
  • Real-world use cases include social networks, biological networks, transportation networks, knowledge graphs, and any domain with relational data.

Quick Start

Create a simple graph G with nodes 1, 2, and 3, and compute its connected components.

Frequently Asked Questions about networkx

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

FAQPage Schema
How do I create and analyze a graph structure in Python for network data?▼

To create and analyze a graph in Python, you can build undirected or directed graph data structures, add nodes and edges, then compute centrality measures, shortest paths, and clustering for relational data analysis.

What is the best way to visualize social or biological networks with Python?▼

Visualizing networks in Python involves generating graph structures and applying built-in visualization utilities to map relationships, allowing you to render social networks, biological networks, and knowledge graphs for analytical insights.

Can I compute centrality and connectivity measures for large transportation networks?▼

Yes, you can compute centrality measures, connectivity, and shortest paths for transportation networks by loading relational data into graph structures and running analytical algorithms to evaluate node importance.

Does Python support MultiGraph and MultiDiGraph for complex network analysis?▼

Python supports MultiGraph and MultiDiGraph structures, allowing you to create, manipulate, and analyze complex graphs with parallel edges for advanced network analysis and visualization workflows.

How do I find connected components in a graph using Python?▼

Finding connected components in Python involves creating a graph object, adding nodes and edges, then calling connectivity algorithms to identify isolated subgraphs within your network data.

What input and output formats work with Python graph analysis workflows?▼

Python network analysis workflows support various input and output formats for graph data, enabling you to import relational datasets, run community detection, and export results for visualization and further research.

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