Please see references below for more about hierarchical random graphs.
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
).
The igraph HRG implementation is heavily based on the code published by Aaron Clauset, at his website (not functional any more).
consensus_tree
,
hrg.consensus
; fit_hrg
,
hrg.fit
; hrg.game
,
sample_hrg
; hrg.predict
,
predict_edges
; hrg_tree
;
hrg
, hrg.create
;
print.igraphHRGConsensus
;
print.igraphHRG