Can I import graph data from CSV or Excel files?

Absolutely. This skill covers reading and writing graphs using edge lists, GraphML, JSON, and direct integration with Pandas DataFrames.

Does this skill support community detection in networks?

Yes, it provides access to various community detection algorithms, including greedy modularity and other partitioning techniques found in the NetworkX library.

What types of graphs can I create with the NetworkX skill?

You can create four primary types: Graph (undirected), DiGraph (directed), MultiGraph (undirected with multiple edges), and MultiDiGraph (directed with multiple edges).

How does the skill handle large-scale network visualizations?

It provides guidance on using efficient layout algorithms like spring or spectral layouts and suggests exporting to vector formats or interactive libraries for high-density networks.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: pur3v4d3r

bypur3v4d3r

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Ciencia de Datos y ML

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

Acerca de

This skill empowers Claude to perform advanced network analysis and graph manipulation within Python environments. It provides specialized guidance for building various graph types, executing complex algorithms like PageRank or community detection, and generating synthetic network models. Whether you are working with social networks, biological systems, or transportation logistics, this skill streamlines the process of extracting structural insights and creating publication-quality visualizations from relationship-based data.

Características Principales

Synthetic network generation including Small-World and Scale-Free models
1 GitHub stars
Extensive algorithm library for centrality, shortest paths, and community detection
Advanced visualization support with customizable layouts and Matplotlib integration
Creation and manipulation of Directed, Undirected, and MultiGraph structures
Seamless data integration with Pandas, NumPy, and SciPy stacks

Casos de Uso

Mapping biological protein-protein interactions or citation networks for research
Analyzing social media connections to identify influencers and community clusters
Modeling and optimizing transportation routes or logistics supply chains

Acerca de

Características Principales

Synthetic network generation including Small-World and Scale-Free models
1 GitHub stars
Extensive algorithm library for centrality, shortest paths, and community detection
Advanced visualization support with customizable layouts and Matplotlib integration
Creation and manipulation of Directed, Undirected, and MultiGraph structures
Seamless data integration with Pandas, NumPy, and SciPy stacks

Casos de Uso

Mapping biological protein-protein interactions or citation networks for research
Analyzing social media connections to identify influencers and community clusters
Modeling and optimizing transportation routes or logistics supply chains