Package edu.uci.ics.jung.algorithms.generators.random

Methods for generating random graphs with various properties. These include:

• BarabasiAlbertGenerator: scale-free graphs using the preferential attachment heuristic.
• EppsteinPowerLawGenerator: graphs whose degree distribution approximates a power law
• ErdosRenyiGenerator: graphs for which edges are created with a specified probability
• MixedRandomGraphGenerator: takes the output of BarabasiAlbertGenerator and perturbs it to generate a mixed-mode analog with both directed and undirected edges.

Class Summary

Class	Description
BarabasiAlbertGenerator<V,E>	Simple evolving scale-free random graph generator.
EppsteinPowerLawGenerator<V,E>	Graph generator that generates undirected graphs with power-law degree distributions.
ErdosRenyiGenerator<V,E>	Generates a random graph using the Erdos-Renyi binomial model (each pair of vertices is connected with probability p).
KleinbergSmallWorldGenerator<V,E>	Graph generator that produces a random graph with small world properties.
MixedRandomGraphGenerator	Generates a mixed-mode random graph (with random edge weights) based on the output of BarabasiAlbertGenerator.