predict_edges
Predict edges based on a hierarchical random graph model
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.
Usage
predict_edges(graph, hrg = NULL, start = FALSE, num.samples = 10000,
num.bins = 25)Arguments
- 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
igraphHRGobject.predict_edgess allow this to beNULLas well, then a HRG is fitted to the graph first, from a random starting point.- start
Logical, whether to start the fitting/sampling from the supplied
igraphHRGobject, or from a random starting point.- 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.
Value
A list with entries:
The predicted edges, in a two-column matrix of vertex ids.
Probabilities of these edges, according to the fitted model.
The (supplied or fitted) hierarchical random graph model.
References
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. Springer-Verlag, Berlin Heidelberg (2007).
See Also
Other hierarchical random graph functions: consensus_tree,
fit_hrg, hrg-methods,
hrg_tree, hrg,
print.igraphHRGConsensus,
print.igraphHRG, sample_hrg
Examples
# NOT RUN {
## 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)
# }