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networkx

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

Updated Mar 15, 2026
Search Tags:#python#algorithms#visualization#data structures#network analysis#graph
One-click install
npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill networkx-sagunkayastha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/sagunkayastha/claude_skills_collection/tree/main/data-analysis-visualization/networkx
Command: npx skills add https://github.com/sagunkayastha/claude_skills_collection --skill networkx-sagunkayastha

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for creating, manipulating, analyzing, and visualizing complex networks and graphs in Python, enabling deep insights into relationships and structures.

Core Features & Use Cases

  • Graph Creation & Manipulation: Build and modify various graph types (undirected, directed, multi-graphs) with attributes.
  • Algorithm Execution: Apply algorithms for shortest paths, centrality, community detection, connectivity, and more.
  • Data Integration: Read from and write to numerous file formats (CSV, GraphML, JSON, etc.) and integrate with Pandas and NumPy.
  • Visualization: Generate static and interactive network visualizations.
  • Use Case: Analyze a social network to identify influential users, map protein-protein interactions, or visualize transportation routes.

Quick Start

Use the networkx skill to load the graph from 'social_network.graphml' and calculate the betweenness centrality for all nodes.

Frequently Asked Questions about networkx

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

FAQPage Schema
How do I analyze a complex network to identify influential users in a social graph?▼

To analyze a complex network for influential users, apply centrality measures and shortest path algorithms to your graph. This reveals structural relationships and identifies key nodes based on their connectivity within the network.

What is the best way to visualize complex networks and graph structures in Python?▼

The best way to visualize complex networks in Python is by using a dedicated graph toolkit that generates static and interactive visualizations. This allows you to visually map out nodes and edges for comprehensive network analysis.

How do I import graph data from CSV, JSON, and GraphML files for network analysis?▼

You can import graph data from CSV, JSON, and GraphML files by reading the formats directly into a graph structure. This integration allows you to seamlessly load external datasets for immediate manipulation and analysis.

Can I use Pandas and NumPy data structures for graph manipulation and algorithm execution?▼

Yes, you can use Pandas and NumPy data structures for graph manipulation and algorithm execution. Integrating these data formats allows you to apply shortest paths, community detection, and connectivity analysis directly.

Does Python support creating directed graphs and multi-graphs with custom attributes?▼

Python supports creating directed graphs and multi-graphs with custom attributes through specialized graph manipulation libraries. You can build and modify various graph types while assigning specific attributes to nodes and edges.

What algorithms are available for community detection and connectivity analysis in network graphs?▼

Available algorithms for community detection and connectivity analysis include shortest paths and centrality measures. These algorithms allow you to execute deep structural analysis to identify clusters and relationships within the graph.

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