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 occurred 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()