networkx

Create and analyze complex networks using NetworkX, matplotlib, and pandas.

Updated May 10, 2026

Search Tags:#community #algorithms #visualization #graphs #network analysis #centrality

Ready to run

npx skills add https://github.com/Imad-Oute/ResearchForge --skill networkx-imad-oute

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/networkx
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill networkx-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

NetworkX simplifies the process of building, analyzing, and visualizing complex networks, reducing the technical barrier for graph-based research and development.

Core Features & Use Cases

Graph Construction: Easily create various types of graphs, add nodes and edges with attributes, and manage network data.
Graph Algorithms: Perform advanced analyses including shortest paths, centrality measures, community detection, and network flows.
Visualization: Generate detailed network diagrams with customization options, supporting publication-quality figures and interactive displays.
Use Case: A social scientist can model social interactions, analyze influence pathways, and visualize community structures all within a single platform.

Quick Start

Load a graph from an edge list file and compute its degree centrality.

Frequently Asked Questions about networkx

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

FAQPage Schema

How do I analyze and visualize complex network graphs for research?▼

To analyze and visualize complex network graphs, you can load network data from an edge list, compute centrality measures or community structures, and generate detailed diagrams using matplotlib for publication-quality figures.

Can I compute centrality measures and detect communities in social network data?▼

Yes, you can compute centrality measures and detect communities in social network data by applying built-in graph algorithms to model social interactions and analyze influence pathways within the network structure.

What's the best way to create a graph from an edge list and calculate degree centrality?▼

The best way to create a graph from an edge list and calculate degree centrality is to load the file into a graph object, add nodes and edges with attributes, and then apply the degree centrality algorithm directly.

Do I need pandas and matplotlib to handle network data and generate network diagrams?▼

You need pandas for network data handling and matplotlib to generate detailed network diagrams with customization options, while networkx is required for the underlying graph construction and algorithm operations.

Does this approach work for analyzing biological systems and communication infrastructure?▼

Yes, this approach works for analyzing biological systems and communication infrastructure because the graph operations support diverse complex network structures, allowing you to model and visualize relationships across these domains.

Can I export network analysis results to generate publication-quality figures?▼

You can generate publication-quality figures from network analysis results by using matplotlib visualization capabilities to create detailed network diagrams with extensive customization options for interactive displays.