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networkx

Create, analyze, and visualize graphs and networks with NetworkX.

21|1|Updated Mar 19, 2026
Search Tags:#python#visualization#graphs#networkx#centrality#shortest-path#graph-analysis
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill networkx-ownlabai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: networkx
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/networkx
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill networkx-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Graphs and networks are central to many data problems. NetworkX provides a pure-Python toolkit to create, manipulate, analyze, and visualize complex networks with rich node and edge attributes, enabling researchers and developers to model relationships, run analyses, and generate insights.

Core Features & Use Cases

  • Graph creation and manipulation: supports Graph, DiGraph, and MultiGraph variants with attributes on nodes and edges.
  • Graph algorithms: compute shortest paths, centrality measures, clustering coefficients, connectivity, community detection, and more.
  • Input/Output and visualization: read/write graphs in GraphML, GraphJSON (node-link), CSV, and visualize via matplotlib or interactive libraries.
  • Use Case: model a social network, analyze connectivity, identify influential nodes, and visualize communities.

Quick Start

Install NetworkX, import as nx, create a graph, and run built-in algorithms to explore a small network.

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 nodes in Python?▼

To analyze a social network in Python, build a graph with nodes and edges, then compute centrality measures to identify influential nodes. This models relationships and reveals structural connectivity patterns within the dataset.

What is the best way to find the shortest path in a directed graph using Python?▼

Finding the shortest path in a directed graph involves creating a DiGraph structure and applying a shortest path algorithm. This calculates the most efficient route between nodes based on edge connections and network topology.

Can I visualize graph communities and export the results to GraphML or CSV?▼

Visualizing graph communities and exporting to GraphML or CSV is fully supported. After detecting communities, render the network visually using matplotlib, and write the structured graph data to standard I/O formats.

Does NetworkX support graphs with multiple edges between the same nodes?▼

Graphs with multiple edges between the same nodes are supported via the MultiGraph variant. This allows modeling complex network relationships where multiple distinct connections exist between identical node pairs.

How do I compute clustering coefficients and detect communities in a network?▼

Computing clustering coefficients and detecting communities involves applying built-in graph algorithms to a constructed network. These algorithms evaluate local connectivity and group nodes into clusters based on structural relationships.

What are the limitations of pure-Python graph analysis for large data science tasks?▼

Pure-Python graph analysis for large data science tasks may face performance constraints with massive networks due to memory overhead. It handles complex attribute manipulation but may lack the parallel processing speed of specialized alternatives.

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