Is it suitable for very large networks?

While highly versatile, performance depends on available memory. It includes sparse matrix support and some approximate algorithms to handle larger datasets efficiently.

Can I import existing network data from other files?

Yes, the skill supports various formats including GraphML, GML, JSON, CSV, edge lists, and direct integration with Pandas DataFrames and NumPy arrays.

What types of graphs can I create with this skill?

You can create undirected graphs, directed graphs (DiGraph), and versions of both that allow multiple edges between nodes (MultiGraph and MultiDiGraph).

Does this skill support network visualization?

Absolutely. It integrates with Matplotlib for drawing and provides multiple layout algorithms like spring, circular, Kamada-Kawai, and spectral layouts for clear visualization.

What kind of algorithms are included in NetworkX?

It includes a vast range of algorithms for shortest paths, centrality measures (like PageRank and Betweenness), community detection, clustering, and connectivity analysis.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: ricable

byricable

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数据科学与机器学习

Builds, analyzes, and visualizes complex graph structures and network data using the powerful NetworkX Python library.

关于

NetworkX Graph Analysis provides a comprehensive suite of tools for working with complex network data within Claude Code. It enables developers and data scientists to create various graph types—including directed, undirected, and multigraphs—while offering a wide array of built-in algorithms for computing centrality, detecting communities, and finding shortest paths. Whether you're modeling social networks, biological systems, or infrastructure topologies, this skill simplifies the process of generating synthetic networks, performing structural analysis, and creating publication-quality visualizations directly through the AI agent.

主要功能

1 GitHub stars
Built-in synthetic network generators for random, small-world, and scale-free models
Extensive library of graph algorithms including PageRank, Dijkstra, and Modularity
Advanced visualization capabilities with various layout algorithms and export options
Support for Directed, Undirected, MultiGraph, and MultiDiGraph structures
Seamless integration with Pandas, NumPy, and SciPy for data manipulation

使用场景

Optimizing transportation or logistics routes using shortest path algorithms
Modeling biological interactions or chemical structures in scientific research
Analyzing social network influence and community structures

关于

主要功能

1 GitHub stars
Built-in synthetic network generators for random, small-world, and scale-free models
Extensive library of graph algorithms including PageRank, Dijkstra, and Modularity
Advanced visualization capabilities with various layout algorithms and export options
Support for Directed, Undirected, MultiGraph, and MultiDiGraph structures
Seamless integration with Pandas, NumPy, and SciPy for data manipulation

使用场景

Optimizing transportation or logistics routes using shortest path algorithms
Modeling biological interactions or chemical structures in scientific research
Analyzing social network influence and community structures