Is community detection supported?

Yes, it includes algorithms for community detection such as greedy modularity communities, as well as connectivity and clustering analysis tools.

Can I visualize networks with this skill?

Yes, the skill provides comprehensive guidance on using Matplotlib and various layout algorithms like Spring, Circular, and Kamada-Kawai to create network visualizations.

How does it handle data import and export?

It supports a wide range of formats including Edge Lists, GraphML, GML, JSON, and CSV, as well as direct integration with Pandas DataFrames and NumPy arrays.

Can I generate synthetic graphs for testing?

Absolutely. It supports various generators including Erdős-Rényi random graphs, Barabási-Albert scale-free networks, and Watts-Strogatz small-world models.

What graph types does this skill support?

The NetworkX skill supports four main types: Graph (undirected), DiGraph (directed), MultiGraph (undirected with multiple edges), and MultiDiGraph (directed with multiple edges).

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: Yezez9

byYezez9

0•

Data Science & ML

Analyzes and visualizes complex networks and graph data structures using the comprehensive Python NetworkX library.

This skill empowers Claude to perform advanced manipulation and analysis of complex networks by leveraging the NetworkX Python package. It provides specialized guidance for creating undirected, directed, and multi-graphs, computing sophisticated metrics like centrality and PageRank, and generating synthetic network models. Whether you are analyzing social network structures, biological pathways, or transportation systems, this skill streamlines the implementation of graph algorithms, data I/O, and high-quality visualizations to extract meaningful insights from pairwise relationships.

Key Features

01Customizable network visualization using various layout algorithms and Matplotlib.

02Advanced algorithm suite including shortest paths, centrality, and community detection.

03Comprehensive graph creation for directed, undirected, and multi-edge networks.

04Synthetic network generation for scale-free, small-world, and random models.

050 GitHub stars

06Flexible I/O support for GraphML, GML, JSON, and Pandas integration.

Use Cases

01Analyzing social network influence and detecting community structures.

02Optimizing logistics and transportation route efficiency using pathfinding algorithms.

03Modeling biological interactions and protein-protein networks for research.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yezez9/research-agent networkx

For use in Claude.ai and ChatGPT