# R igraph manual pages

Use this if you are using igraph from R

## Hierarchical random graphs

### Description

Fitting and sampling hierarchical random graph models.

### Details

A hierarchical random graph is an ensemble of undirected graphs with n vertices. It is defined via a binary tree with n leaf and n-1 internal vertices, where the internal vertices are labeled with probabilities. The probability that two vertices are connected in the random graph is given by the probability label at their closest common ancestor.

igraph contains functions for fitting HRG models to a given network (fit_hrg, for generating networks from a given HRG ensemble (sample_hrg), converting an igraph graph to a HRG and back (hrg, hrg_tree), for calculating a consensus tree from a set of sampled HRGs (consensus_tree) and for predicting missing edges in a network based on its HRG models (predict_edges).
Other hierarchical random graph functions: consensus_tree(), fit_hrg(), hrg_tree(), hrg(), predict_edges(), print.igraphHRGConsensus(), print.igraphHRG(), sample_hrg()