EngineersofAI LogoCoursesBlogResearch LabAI LettersThe LabCode BankInteractive 3DKodrEarnestJobs
Skip to main content
Interactive 3D/Random Graphs
G(20, 0.15) - Erdős-Rényi  |  nodes colored by component
Model
Parameters
n nodes20
p (edge prob)0.15
Statistics
Nodes: 20
Edges: 0
Avg degree: 0.00
Components: 20
Giant component: 1 (5%)
Clustering coeff: 0.0000
Theory avg deg: 3.00
Key Insight
Phase transition at p = 1/n = 0.050. Giant component emerges when avg deg > 1.

Random Graphs - Interactive Visualization

Random graph models explain network structure. Erdős-Rényi G(n,p) adds each edge independently with probability p - around p = ln(n)/n, a giant connected component suddenly appears (phase transition). Barabási-Albert uses preferential attachment (rich get richer) to generate scale-free networks with power-law degree distributions, matching real social and citation networks.

  • Watch Erdős-Rényi graph form as p increases from 0 to 1
  • See the phase transition: giant component appears around p = ln(n)/n
  • Switch to Barabási-Albert scale-free model and see hubs emerge
  • Compare degree distributions: Poisson (ER) vs power-law (BA)
  • Foundation for graph generation models, link prediction, and network robustness

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.