Is it suitable for very large networks?

NetworkX is highly flexible but memory-intensive. This skill provides best practices for using sparse matrices and approximate algorithms to handle performance constraints.

Does it include visualization capabilities?

Yes, it includes guidance for Matplotlib-based drawing, custom layouts like Spring and Kamada-Kawai, and exporting publication-quality figures in PNG and PDF.

What types of graphs does the NetworkX skill support?

It supports undirected (Graph), directed (DiGraph), and multigraphs (MultiGraph, MultiDiGraph), allowing for complex relationship modeling across various domains.

Can I import graph data from existing files?

Yes, the skill provides patterns for reading and writing multiple formats including GraphML, GML, JSON, edge lists, and adjacency matrices, as well as CSV via Pandas integration.

How does this skill improve Claude's coding output?

It provides Claude with domain-specific guidance and validated implementation patterns, ensuring that complex graph algorithms and data structures are implemented correctly and efficiently.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: Sologa

bySologa

Data Science & ML

Provides a comprehensive toolkit for building, analyzing, and visualizing complex network structures and graph algorithms in Python.

About

The NetworkX skill empowers Claude to efficiently handle complex graph-based data structures, enabling advanced network analysis, community detection, and synthetic graph generation within a Python environment. It is an essential capability for projects involving social network analysis, biological pathfinding, transportation systems, and knowledge graph construction, offering standardized implementation patterns for everything from basic node manipulation to sophisticated algorithmic computations and publication-quality visualizations.

Key Features

Implementation of core algorithms including PageRank, Dijkstra's, and community detection.
Advanced visualization support with multiple layout algorithms and Matplotlib customization.
Seamless integration with Pandas, NumPy, and SciPy for data-driven analysis.
Comprehensive graph creation for undirected, directed, and multi-edge networks.
Synthetic network generation for testing scale-free and small-world models.
0 GitHub stars

Use Cases

Optimizing logistics and transportation routing using shortest path algorithms.
Analyzing social media influence and community structures through centrality measures.
Modeling biological interactions and citation networks in research and bioinformatics.

About

Key Features

Implementation of core algorithms including PageRank, Dijkstra's, and community detection.
Advanced visualization support with multiple layout algorithms and Matplotlib customization.
Seamless integration with Pandas, NumPy, and SciPy for data-driven analysis.
Comprehensive graph creation for undirected, directed, and multi-edge networks.
Synthetic network generation for testing scale-free and small-world models.
0 GitHub stars

Use Cases

Optimizing logistics and transportation routing using shortest path algorithms.
Analyzing social media influence and community structures through centrality measures.
Modeling biological interactions and citation networks in research and bioinformatics.