Use this if you are using igraph from R
predict_edges {igraph}  R Documentation 
predict_edges
uses a hierarchical random graph model to predict
missing edges from a network. This is done by sampling hierarchical models
around the optimum model, proportionally to their likelihood. The MCMC
sampling is stated from hrg
, if it is given and the start
argument is set to TRUE
. Otherwise a HRG is fitted to the graph
first.
predict_edges(
graph,
hrg = NULL,
start = FALSE,
num.samples = 10000,
num.bins = 25
)
graph 
The graph to fit the model to. Edge directions are ignored in directed graphs. 
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. 
num.bins 
Number of bins for the edge probabilities. Give a higher number for a more accurate prediction. 
A list with entries:
edges 
The predicted edges, in a twocolumn matrix of vertex ids. 
prob 
Probabilities of these edges, according to the fitted model. 
hrg 
The (supplied or fitted) hierarchical random graph model. 
A. Clauset, C. Moore, and M.E.J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008);
A. Clauset, C. Moore, and M.E.J. Newman. Structural Inference of Hierarchies in Networks. In E. M. Airoldi et al. (Eds.): ICML 2006 Ws, Lecture Notes in Computer Science 4503, 1–13. SpringerVerlag, Berlin Heidelberg (2007).
Other hierarchical random graph functions:
consensus_tree()
,
fit_hrg()
,
hrgmethods
,
hrg_tree()
,
hrg()
,
print.igraphHRGConsensus()
,
print.igraphHRG()
,
sample_hrg()
## We are not running these examples any more, because they
## take a long time (~15 seconds) to run and this is against the CRAN
## repository policy. Copy and paste them by hand to your R prompt if
## you want to run them.
## Not run:
## A graph with two dense groups
g < sample_gnp(10, p=1/2) + sample_gnp(10, p=1/2)
hrg < fit_hrg(g)
hrg
## The consensus tree for it
consensus_tree(g, hrg=hrg, start=TRUE)
## Prediction of missing edges
g2 < make_full_graph(4) + (make_full_graph(4)  path(1,2))
predict_edges(g2)
## End(Not run)