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Barabási–Albert Network Model Visualization

Curran Kelleher

Last edited Jun 17, 2025
Created on Jun 17, 2025

Barabási–Albert Network Model Visualization

This visualization demonstrates the Barabási–Albert model for generating scale-free networks using preferential attachment. The algorithm adds a new node to the network every 500ms, connecting it to e

Based on an old implementation.

How it works

  1. The simulation starts with two connected nodes
  2. Every 500ms, a new node is added to the network
  3. The new node connects to existing nodes with probability proportional to their degree (preferential attachment)
  4. This creates a scale-free network where some nodes become highly connected "hubs"

Features

  • Interactive force-directed graph visualization using D3.js
  • Nodes can be dragged to explore the network topology
  • Auto-scaling as the network grows
  • Real-time visualization of the preferential attachment process
  • New nodes are positioned randomly within the visual space

This is an implementation of the Barabási–Albert model described at: http://en.wikipedia.org/wiki/BA_model

MIT Licensed