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RamanEbrahimi
RamanEbrahimiCOMMUNITY
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networkx

Create, analyze, and visualize complex networks and graphs in Python.

3|Updated Apr 17, 2026
Search Tags:#network visualization#network analysis#graph algorithms#social network analysis#graph data structure
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill networkx-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/networkx
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill networkx-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires networkx, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for analyzing and visualizing complex networks and graphs in Python, enabling users to explore relationships between entities, compute graph algorithms, and generate synthetic networks.

Core Features & Use Cases

  • Graph Creation and Manipulation: Build and modify various graph types (Graph, DiGraph, MultiGraph, MultiDiGraph).
  • Graph Analysis: Compute centrality measures, find shortest paths, detect communities, and measure clustering.
  • Graph Algorithms: Run standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow.
  • Network Generation: Create synthetic networks for testing or simulation.
  • Graph I/O: Read from and write to various formats (edge lists, GraphML, JSON, CSV, adjacency matrices).
  • Visualization: Draw and customize network visualizations with matplotlib or interactive libraries.
  • Use Case: Suppose you have a social network with nodes representing individuals and edges representing friendships. Use this Skill to analyze the network's structure, identify influential individuals, and visualize the network layout.

Quick Start

Use the networkx skill to compute the shortest path between nodes 1 and 5 in the graph defined by 'networkx.gml'.

Frequently Asked Questions about networkx

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

FAQPage Schema
How do I analyze a social network and identify influential individuals in Python?▼

To analyze a social network, you can compute centrality measures on a graph to identify influential individuals. This skill supports calculating centrality, detecting communities, and visualizing the network layout using matplotlib.

What graph algorithms can I run for shortest path and maximum flow calculations?▼

You can run standard graph algorithms including Dijkstra's for shortest paths, PageRank, minimum spanning trees, and maximum flow. These algorithms process graph data structures to compute routing and network influence metrics.

Can I read and write graph data structures from edge lists and GraphML formats?▼

Yes, you can read from and write to various formats including edge lists, GraphML, JSON, CSV, and adjacency matrices. This graph I/O capability allows you to easily load and save network data for analysis.

Does this network analysis skill support directed graphs and weighted edges?▼

Yes, this skill supports building and modifying various graph types including DiGraph and MultiDiGraph. You can create directed graphs to model asymmetric pairwise relationships in transportation or citation networks.

What's the best way to visualize complex networks and customize the graph layout?▼

The best way to visualize complex networks is by drawing and customizing layouts with matplotlib. You can generate network visualizations to explore the structure of biological or social networks directly in Python.

How do I generate synthetic networks for testing graph algorithms?▼

You can create synthetic networks for testing or simulation using built-in network generation features. This allows you to produce random graph data structures to validate algorithms before deploying them on real datasets.

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