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