Use this if you are using igraph from R
consensus_tree
creates a consensus tree from several fitted
hierarchical random graph models, using phylogeny methods. If the hrg
argument is given and start
is set to TRUE
, then it starts
sampling from the given HRG. Otherwise it optimizes the HRG loglikelihood
first, and then samples starting from the optimum.
consensus_tree(graph, hrg = NULL, start = FALSE, num.samples = 10000)
graph 
The graph the models were fitted to. 
hrg 
A hierarchical random graph model, in the form of an

start 
Logical, whether to start the fitting/sampling from the
supplied 
num.samples 
Number of samples to use for consensus generation or missing edge prediction. 
consensus_tree
returns a list of two objects. The first
is an igraphHRGConsensus
object, the second is an
igraphHRG
object. The igraphHRGConsensus
object has the
following members:
parents 
For each vertex, the id of its parent vertex is stored, or zero, if the vertex is the root vertex in the tree. The first n vertex ids (from 0) refer to the original vertices of the graph, the other ids refer to vertex groups. 
weights 
Numeric vector, counts the number of times a given tree
split occured in the generated network samples, for each internal
vertices. The order is the same as in the 
Other hierarchical random graph functions: fit_hrg
,
hrgmethods
, hrg_tree
,
hrg
, predict_edges
,
print.igraphHRGConsensus
,
print.igraphHRG
, sample_hrg