What graph types does this skill support?

It supports four main graph types: undirected (Graph), directed (DiGraph), and versions of both that allow multiple edges between nodes (MultiGraph and MultiDiGraph).

Can I use this with data from Excel or CSV?

Yes, the skill provides implementation patterns for importing edge lists from CSV files and integrating directly with Pandas DataFrames to build network structures.

Does it handle large-scale networks?

While NetworkX is highly versatile, the skill includes guidance on memory management, sparse matrix structures, and the use of approximate algorithms for very large graphs.

Can Claude generate visualizations with this skill?

Yes, the skill includes best practices for creating network plots using Matplotlib and layout algorithms like Spring or Kamada-Kawai for publication-quality figures.

What kind of algorithms are included?

The skill covers a wide range of graph algorithms including shortest paths (Dijkstra), centrality measures (PageRank, betweenness), community detection, and connectivity analysis.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: Weiwei-Mao

byWeiwei-Mao

データサイエンスとML

Analyzes and visualizes complex network structures and graph data within Python environments.

概要

This skill integrates the comprehensive capabilities of the NetworkX library into Claude, enabling sophisticated graph theory applications and network science workflows. It provides a specialized toolkit for creating, manipulating, and studying the structure, dynamics, and functions of complex networks. Whether you are working with social network analysis, biological pathways, transportation systems, or knowledge graphs, this skill offers optimized patterns for implementing algorithms like PageRank, community detection, and shortest path calculations, while facilitating seamless data integration with Pandas and high-quality visualization.

主な機能

Advanced network algorithms including centrality, connectivity, and community detection
Built-in generators for classic, random, scale-free, and small-world network models
Extensive I/O support for GraphML, GML, JSON, and Pandas DataFrames
0 GitHub stars
Comprehensive graph creation for directed, undirected, and multi-graphs
Customizable network visualization and force-directed layout algorithms

ユースケース

Optimizing routing and connectivity in transportation or communication systems
Modeling and analyzing biological pathways or citation networks with complex relationships
Analyzing influence, centrality, and community structures in social networks

概要

主な機能

Advanced network algorithms including centrality, connectivity, and community detection
Built-in generators for classic, random, scale-free, and small-world network models
Extensive I/O support for GraphML, GML, JSON, and Pandas DataFrames
0 GitHub stars
Comprehensive graph creation for directed, undirected, and multi-graphs
Customizable network visualization and force-directed layout algorithms

ユースケース

Optimizing routing and connectivity in transportation or communication systems
Modeling and analyzing biological pathways or citation networks with complex relationships
Analyzing influence, centrality, and community structures in social networks