Random graphs following a distribution model the mechanism by which the graph is formed, such as adding links to a web page or citations to a paper. These distributions make it possible to study simulated internets, communication networks, citation graphs, social networks, etc.  Building on its strong capabilities for distributions, the Wolfram Language provides cohesive and comprehensive random graph support. Using a symbolic representation of a graph distribution makes it easy to simulate its behavior and compute probabilities of its properties.

Simulation

RandomGraph — simulate a random graph

Parametric Graph Distributions

BernoulliGraphDistribution — Bernoulli graph distribution

BarabasiAlbertGraphDistribution — scale-free graph distribution

DegreeGraphDistribution  ▪  PriceGraphDistribution  ▪  SpatialGraphDistribution  ▪  UniformGraphDistribution  ▪  WattsStrogatzGraphDistribution

Property Distribution

GraphPropertyDistribution — distribution of a graph property

Probability and Statistics »

NProbability — compute probabilities for graph properties

NExpectation — compute expectations for graph properties

RandomVariate  ▪  Probability  ▪  Expectation  ▪  Distributed  ▪  ...