Can I visualize networks with this skill?

Yes, the skill provides extensive documentation and code patterns for visualizing networks using Matplotlib, including spring, circular, and spectral layouts.

Is this skill suitable for large-scale network data?

While NetworkX is memory-efficient, for extremely large networks, the skill provides guidance on using sparse matrices and approximate algorithms to maintain performance.

What graph types does this skill support?

It supports four main graph types: Undirected Graphs, Directed Graphs (DiGraph), MultiGraphs (undirected with multiple edges), and MultiDiGraphs (directed with multiple edges).

Does it integrate with data analysis libraries like Pandas?

Absolutely. It supports seamless conversion between NetworkX graphs and Pandas DataFrames, as well as NumPy arrays and SciPy sparse matrices.

What file formats can I use to import data?

You can read and write network data using edge lists, GraphML, GML, JSON, and CSV files, ensuring compatibility with most existing data pipelines.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: Activer007

byActiver007

データサイエンスとML

Creates, analyzes, and visualizes complex network structures and graph algorithms using the NetworkX library in Python.

概要

NetworkX is a powerful Python toolkit designed for the creation, manipulation, and study of complex networks. This skill enables Claude to perform sophisticated graph analysis, including computing centrality measures, finding shortest paths, detecting communities, and generating synthetic networks like scale-free or small-world models. It is an essential tool for developers and researchers working with social networks, biological data, transportation systems, or any domain involving pairwise relationships, offering deep integration with Pandas, NumPy, and Matplotlib for a complete data science workflow.

主な機能

Advanced graph algorithms including PageRank, Dijkstra's, and centrality measures
Synthetic network generation for testing and simulation (Erdős-Rényi, Watts-Strogatz)
Detection of communities and modularity analysis for social networks
Seamless I/O support for GraphML, GML, JSON, CSV, and Pandas DataFrames
Publication-quality network visualization with customizable layouts
0 GitHub stars

ユースケース

Modeling biological interaction networks or citation graphs for academic research
Optimizing logistics and transportation systems through shortest-path and flow analysis
Analyzing social networks to identify key influencers and community clusters

概要

主な機能

Advanced graph algorithms including PageRank, Dijkstra's, and centrality measures
Synthetic network generation for testing and simulation (Erdős-Rényi, Watts-Strogatz)
Detection of communities and modularity analysis for social networks
Seamless I/O support for GraphML, GML, JSON, CSV, and Pandas DataFrames
Publication-quality network visualization with customizable layouts
0 GitHub stars

ユースケース

Modeling biological interaction networks or citation graphs for academic research
Optimizing logistics and transportation systems through shortest-path and flow analysis
Analyzing social networks to identify key influencers and community clusters