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networkx

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

13|3|Updated Jun 10, 2026
Search Tags:#network visualization#graph analysis#networkx#graph algorithms#network data structures
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill networkx-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/networkx
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill networkx-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires networkx, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for analyzing and visualizing complex networks and graphs in Python, enabling users to work with network data structures, compute graph algorithms, and generate synthetic networks for testing or simulation.

Core Features & Use Cases

  • Graph Creation and Manipulation: Build and modify various graph types (Graph, DiGraph, MultiGraph, MultiDiGraph).
  • Graph Analysis: Compute centrality measures, find shortest paths, detect communities, and measure clustering.
  • Graph Algorithms: Run standard algorithms like Dijkstra's, PageRank, minimum spanning trees, and maximum flow.
  • Network Generation: Create synthetic networks for testing and simulation.
  • Graph I/O: Read from and write to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices).
  • Visualization: Draw and customize network visualizations with matplotlib or interactive libraries.
  • Use Case: Imagine you have a social network dataset. Use this Skill to analyze the network structure, compute centrality measures, and visualize the network topology.

Quick Start

Use the networkx skill to create a graph from an edge list and compute the shortest path between two nodes.

Frequently Asked Questions about networkx

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

FAQPage Schema
How do I compute shortest paths and centrality measures for graph analysis in Python?▼

Graph analysis in Python is performed by applying algorithms like Dijkstra's for shortest paths and centrality measures to network data structures. This provides insights into node importance and connectivity within complex networks.

Can I create and manipulate directed graphs and multigraphs for network data structures?▼

Yes, you can create and manipulate network data structures including Graph, DiGraph, MultiGraph, and MultiDiGraph. This allows you to model complex pairwise relationships and directed flows within transportation or biological networks.

What is the best way to visualize network topology and social network datasets?▼

Visualizing network topology involves drawing and customizing graphs using matplotlib or interactive libraries. This translates structural graph analysis into visual representations of nodes and edges for social network datasets.

Does Python support reading and writing graph I/O formats like GraphML, JSON, and edge lists?▼

Python supports graph I/O by reading from and writing to formats including edge lists, GraphML, JSON, CSV, and adjacency matrices. This enables seamless integration of graph data structures with external data sources.

How do I generate synthetic networks for testing graph algorithms and simulations?▼

You can generate synthetic networks to create testbeds for graph algorithms and simulations. This allows you to produce controlled network data structures to evaluate performance before deploying on real-world complex networks.

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