Can this skill handle directed and undirected graphs?

Yes, it fully supports Graph (undirected), DiGraph (directed), MultiGraph, and MultiDiGraph types for complex relational modeling.

Can I import data from CSVs or DataFrames?

Absolutely. The skill includes methods to convert Pandas DataFrames into graph structures and vice-versa, as well as support for GraphML, GML, and edge lists.

Is this skill suitable for large-scale networks?

While NetworkX is memory-bound, the skill provides guidance on using sparse matrices and approximate algorithms to handle larger datasets efficiently.

Does it support network visualization?

Yes, it integrates with Matplotlib and provides various layout algorithms like Spring, Circular, and Kamada-Kawai to create informative network visualizations.

What kind of graph algorithms are available?

It includes a wide range of standard algorithms such as Dijkstra's shortest path, PageRank, betweenness centrality, community detection, and connectivity testing.

NetworkX Graph Analysis

Name: NetworkX Graph Analysis
Author: K-Dense-AI

byK-Dense-AI

•

3,718

Data Science & ML

Builds, analyzes, and visualizes complex network structures and graph algorithms using Python's NetworkX library.

About

The NetworkX skill empowers Claude to handle complex graph-based data structures, enabling the creation, manipulation, and study of relational networks such as social graphs, biological pathways, and transportation systems. It provides high-level functions for calculating centrality measures, finding shortest paths, detecting communities, and generating synthetic network models, while also facilitating seamless data exchange between pandas, NumPy, and various graph file formats like GraphML and GML. This skill is ideal for any domain involving pairwise relationships where structural analysis and topology visualization are required.

Key Features

Extensive library of algorithms for centrality, connectivity, and shortest paths
Synthetic network generation for testing scale-free and small-world models
Advanced community detection and modularity clustering tools
3,718 GitHub stars
Seamless integration with Pandas and NumPy for high-performance data handling
Support for directed, undirected, and multi-edge graph types

Use Cases

Optimizing logistics, transportation routes, and supply chain networks
Analyzing social network influence, degree centrality, and community structures
Modeling biological interactions and knowledge graphs for scientific research

About

Key Features

Extensive library of algorithms for centrality, connectivity, and shortest paths
Synthetic network generation for testing scale-free and small-world models
Advanced community detection and modularity clustering tools
3,718 GitHub stars
Seamless integration with Pandas and NumPy for high-performance data handling
Support for directed, undirected, and multi-edge graph types

Use Cases

Optimizing logistics, transportation routes, and supply chain networks
Analyzing social network influence, degree centrality, and community structures
Modeling biological interactions and knowledge graphs for scientific research