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networkx

Create, analyze, and visualize network graphs using Python and NetworkX.

7|1|Updated Jan 14, 2026
Search Tags:#python#algorithms#visualization#network analysis#graph analysis#networkx
One-click install
npx skills add https://github.com/eyadsibai/ltk --skill networkx-eyadsibai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/eyadsibai/ltk/tree/main/plugins/ltk-data/skills/networkx
Command: npx skills add https://github.com/eyadsibai/ltk --skill networkx-eyadsibai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides tools and knowledge for analyzing complex relationships within data, enabling users to understand network structures, identify key nodes, and discover patterns.

Core Features & Use Cases

  • Graph Creation: Build various types of graphs (directed, undirected, multi-graphs).
  • Algorithm Implementation: Utilize algorithms for centrality, shortest paths, and community detection.
  • Data Visualization: Generate visual representations of network structures.
  • Use Case: Analyze a social media network to identify influential users (PageRank) or find the shortest communication path between two individuals.

Quick Start

Use the networkx skill to generate a random graph with 100 nodes and 500 edges.

Frequently Asked Questions about networkx

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

FAQPage Schema
How do I analyze a social network to find the most influential users?▼

Social network analysis identifies influential users by applying centrality algorithms like PageRank to graph data structures. You can build network graphs and execute algorithms to pinpoint key nodes and structural patterns within your social media data.

Can I build directed and undirected graphs for complex system modeling in Python?▼

Yes, you can build directed, undirected, and multi-graphs in Python for complex system modeling. This Skill facilitates the creation of various graph structures to represent and analyze intricate relationships within your network data.

What is the best way to visualize network data structures?▼

Visualizing network data structures is best handled by generating visual representations of nodes and edges. This Skill provides the capability to create these visual outputs, helping you understand complex network topologies and relationship patterns.

How do I find the shortest communication path between two nodes in a network?▼

Finding the shortest communication path requires executing shortest path algorithms on a graph structure. This Skill implements these algorithms, allowing you to calculate the most efficient routes between any two nodes in your network.

Do I need the NetworkX library to perform graph analysis?▼

Yes, you need the NetworkX library to perform graph analysis with this Skill. It requires the NetworkX library for underlying graph manipulation, algorithm execution, and the generation of random graphs for your analysis.

Can I detect communities within a knowledge graph?▼

Yes, you can detect communities within a knowledge graph by utilizing community detection algorithms. This Skill supports algorithm implementation for knowledge graphs, helping you discover clustered groups and patterns in complex network data.

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