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.
RandomGraph — simulate a random graph
BernoulliGraphDistribution — Bernoulli graph distribution
BarabasiAlbertGraphDistribution — scale-free graph distribution
GraphPropertyDistribution — distribution of a graph property
NProbability — compute probabilities for graph properties
NExpectation — compute expectations for graph properties