What types of graphs does this skill support?

It supports four main graph types: Graph (undirected), DiGraph (directed), MultiGraph (undirected with multiple edges), and MultiDiGraph (directed with multiple edges).

Can I create visualizations for my research papers?

Absolutely. The skill includes advanced visualization features using Matplotlib and various layout algorithms to produce publication-quality network diagrams in formats like PNG and PDF.

Can I use this skill with existing Pandas data?

Yes, it includes built-in functions like from_pandas_edgelist and to_pandas_edgelist to convert between Pandas DataFrames and NetworkX objects seamlessly.

Does it handle large-scale network analysis?

While NetworkX is memory-based, it supports integration with SciPy sparse matrices and provides approximate algorithms to handle larger datasets efficiently.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: Microck

byMicrock

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数据科学与机器学习

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

关于

NetworkX is a comprehensive Claude Code Skill designed for software developers and data scientists working with complex relational data. It enables users to build, manipulate, and analyze graph structures—ranging from social networks and biological systems to transportation grids—by providing access to a vast array of algorithms for centrality, community detection, and shortest paths. Whether you need to generate synthetic networks for testing or export intricate visualizations to publication-quality formats, this skill streamlines the entire network science workflow within the Claude environment.

主要功能

Integrates seamlessly with Pandas, NumPy, and SciPy for data science workflows
81 GitHub stars
Generates synthetic networks using Erdős-Rényi, Barabási-Albert, and small-world models
Implements advanced graph algorithms like PageRank, Dijkstra’s, and community detection
Supports multiple graph types including undirected, directed, and multi-edge graphs
Provides robust visualization tools for creating customizable network topologies and layouts

使用场景

Modeling biological pathways or citation networks to discover structural patterns
Optimizing logistics and transportation routes using shortest path and flow algorithms
Analyzing social media interactions to identify influential nodes and community clusters

关于

主要功能

Integrates seamlessly with Pandas, NumPy, and SciPy for data science workflows
81 GitHub stars
Generates synthetic networks using Erdős-Rényi, Barabási-Albert, and small-world models
Implements advanced graph algorithms like PageRank, Dijkstra’s, and community detection
Supports multiple graph types including undirected, directed, and multi-edge graphs
Provides robust visualization tools for creating customizable network topologies and layouts

使用场景

Modeling biological pathways or citation networks to discover structural patterns
Optimizing logistics and transportation routes using shortest path and flow algorithms
Analyzing social media interactions to identify influential nodes and community clusters