Generate a pseudorandom graph:

GlobalClusteringCoefficient as a function of rewiring probability:

Generate simple undirected graphs:

Generate a set of pseudorandom graphs:

Compute probabilities and statistical properties:

The Western States Power Grid can be modeled with WattsStrogatzGraphDistribution:

The model captures the small-world characteristics of the empirical network, with short mean graph distance and high clustering:

A social network in a village of 100 people where the average number of relations per person is 20 can be modeled using a WattsStrogatzGraphDistribution. Find the expected number of relations for the least-connected person:

The expected number of relations for the least-connected person:

Expected degree separation:

This represents a simplified model for the spread of an infectious disease in a social network. The disease spreads in each step with probability 0.4 from infected individuals to some of their susceptible neighbors, while infected individuals recover and become immune:

Simulate an infection and find infected persons:

Highlight infected persons:

The fraction of infected persons as a function of the transmission probability:

Distribution of the number of vertices:

Distribution of the number of edges:

Distribution of the vertex degree:

Approximate with a sum of BinomialDistribution and PoissonDistribution:

The mean distance decreases quickly as the rewiring probability increases:

The clustering coefficient decreases slowly:

WattsStrogatzGraphDistribution[n,0,k] is a 2k-regular graph:

For n≤2k it is (n-1)-regular:

Randomly colored vertices: