What types of graphs can I create with this skill?

You can create undirected graphs, directed graphs (DiGraphs), and multigraphs that allow multiple edges between the same nodes, all with custom attributes.

Does this skill support community detection?

Yes, it includes patterns for various community detection algorithms such as greedy modularity and Girvan-Newman to identify clusters within networks.

Can I convert my network data into a Pandas DataFrame?

Absolutely. The skill includes specific patterns for converting edge lists and node attributes to and from Pandas DataFrames for easy analysis.

How does this skill handle large datasets?

The skill provides guidance on using SciPy sparse matrices and approximate algorithms to maintain performance when working with large-scale graph structures.

What visualization options are available?

It supports a wide range of layouts including spring (force-directed), circular, and spectral layouts, as well as customization for node size, color, and edge weights.

NetworkX Analysis

Name: NetworkX Analysis
Author: K-Dense-AI

byK-Dense-AI

•

2,188

Data Science & ML

Builds, analyzes, and visualizes complex networks and graph data structures using the comprehensive NetworkX library for Python.

About

The NetworkX skill empowers Claude to perform sophisticated graph theory and network science tasks directly within your development workflow. It provides specialized guidance for creating directed and undirected graphs, implementing advanced algorithms like PageRank and community detection, and generating synthetic network models such as scale-free or small-world networks. Ideal for social network analysis, biological modeling, and logistics optimization, this skill ensures best practices for data I/O with Pandas and NumPy while enabling high-quality visualization and structural analysis.

Key Features

Built-in network generators for Erdős-Rényi, Barabási-Albert, and Watts-Strogatz models.
2,188 GitHub stars
Seamless I/O integration with Pandas DataFrames, NumPy arrays, and GraphML files.
Advanced graph algorithms including shortest paths, centrality measures, and clustering.
Extensive visualization capabilities using force-directed, circular, and spectral layouts.
Comprehensive support for Graph, DiGraph, MultiGraph, and MultiDiGraph structures.

Use Cases

Modeling biological pathways or protein-protein interaction networks for scientific research.
Optimizing transportation or logistics systems using flow and shortest-path algorithms.
Analyzing social networks to identify influencers and community clusters.

About

Key Features

Built-in network generators for Erdős-Rényi, Barabási-Albert, and Watts-Strogatz models.
2,188 GitHub stars
Seamless I/O integration with Pandas DataFrames, NumPy arrays, and GraphML files.
Advanced graph algorithms including shortest paths, centrality measures, and clustering.
Extensive visualization capabilities using force-directed, circular, and spectral layouts.
Comprehensive support for Graph, DiGraph, MultiGraph, and MultiDiGraph structures.

Use Cases

Modeling biological pathways or protein-protein interaction networks for scientific research.
Optimizing transportation or logistics systems using flow and shortest-path algorithms.
Analyzing social networks to identify influencers and community clusters.