networkx-graph-analysis

Name: networkx-graph-analysis
Availability: InStock
Author: jaechang-hits

Create, manipulate, and analyze graphs with NetworkX centrality and pathfinding.

298|27|Updated Feb 18, 2026

Search Tags:#data visualization #python #algorithms #graph theory #network analysis #networkx

Ready to run

npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill networkx-graph-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: networkx-graph-analysis
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/scientific-computing/networkx-graph-analysis
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill networkx-graph-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for creating, manipulating, and analyzing complex networks and graphs, enabling you to understand relationships and structures within your data.

Core Features & Use Cases

Graph Creation: Build various graph types (undirected, directed, multi-edge) with node and edge attributes.
Analysis: Compute centrality measures, find shortest paths, detect communities, and analyze graph properties.
I/O & Visualization: Read/write graphs in multiple formats and visualize network structures.
Use Case: Analyze a social network to identify influential users, detect communities, and visualize the network topology.

Quick Start

Use the networkx-graph-analysis skill to create a graph from an edge list and compute its betweenness centrality.

Frequently Asked Questions about networkx-graph-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze a social network to identify influential users using Python?▼

You can build a graph from an edge list and compute centrality measures, such as betweenness centrality, to identify influential users and understand the network topology using the NetworkX library in Python.

What is the best way to perform community detection and graph visualization on complex networks?▼

Perform community detection and graph visualization by creating graph structures with the NetworkX library, which supports analyzing graph properties and visualizing network topology for complex networks.

Does NetworkX support building directed and multi-edge graphs with custom attributes?▼

NetworkX supports building undirected, directed, and multi-edge graph types, allowing you to assign and manipulate custom node and edge attributes for complex network analysis.

How do I find shortest paths in a complex network using Python?▼

Finding shortest paths in a complex network is done using the NetworkX library in Python, which provides pathfinding algorithms to compute deterministic graph analysis on network structures.

Can I read and write graph data in multiple formats for network analysis?▼

You can read and write graph data in multiple formats using NetworkX I/O operations, enabling you to import edge lists and export graph structures for data science applications.