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Predict edges based on a hierarchical random graph model

Source: R/hrg.R
predict_edges.Rd

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 igraphHRG object. predict_edges() allow this to be NULL as 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 igraphHRG object, 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:

edges

The predicted edges, in a two-column matrix of vertex ids.

prob

Probabilities of these edges, according to the fitted model.

hrg

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(), hrg-methods, hrg_tree(), print.igraphHRG(), print.igraphHRGConsensus(), sample_hrg()

Examples

if (FALSE) { # rlang::is_interactive()

## 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)
}