networkx

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

Updated Aug 23, 2026

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

Ready to run

npx skills add https://github.com/jacketlong23/skills --skill networkx-jacketlong23

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/jacketlong23/skills/tree/main/networkx
Command: npx skills add https://github.com/jacketlong23/skills --skill networkx-jacketlong23

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill empowers you to create, analyze, and visualize complex network and graph data, transforming abstract relationships into actionable insights.

Core Features & Use Cases

Graph Creation & Manipulation: Build and modify various graph types (undirected, directed, multi-graphs).
Advanced Algorithms: Apply algorithms for shortest paths, centrality, community detection, and more.
Data I/O & Visualization: Read/write graphs from/to multiple formats and generate insightful visualizations.
Use Case: Analyze a social network to identify key influencers, map out biological pathways, or visualize a citation network to understand research trends.

Quick Start

Use the networkx skill to create a graph with nodes 1, 2, 3 and edges (1,2), (2,3).

Frequently Asked Questions about networkx

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

FAQPage Schema

How do I analyze complex networks and graph data structures in Python?▼

To analyze complex networks and graph data structures in Python, you can use a comprehensive toolkit to create graphs, compute centrality, detect communities, and apply shortest path algorithms to pairwise relationship data.

What's the best way to visualize network topologies for social or biological networks?▼

The best way to visualize network topologies for social or biological networks is by generating visualizations directly from graph data structures, mapping pairwise relationships to reveal key influencers, pathways, and research trends.

How do I compute shortest paths and centrality for directed and undirected graphs?▼

You compute shortest paths and centrality for directed and undirected graphs by applying advanced graph algorithms to your network data, enabling identification of key nodes and measurement of relationship strength.

Can I build and modify multi-graphs with custom nodes and edges?▼

Yes, you can build and modify multi-graphs with custom nodes and edges. The toolkit supports creating various graph types, allowing you to add nodes, define edges, and manipulate structures for specific network analysis needs.

What Python data formats can I use to read and write graph data?▼

You can read and write graph data using multiple I/O formats supported by the toolkit. This allows you to import existing network topologies and export analyzed graph structures for downstream processing.

When should I use graph analysis algorithms for my data instead of standard analytics?▼

You should use graph analysis algorithms instead of standard analytics when your data involves pairwise relationships between entities, such as transportation systems, citation networks, or biological pathways requiring relationship mapping.