igraph: predict_edges – R documentation

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predict_edges

Predict edges based on a hierarchical random graph model

Description

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`s 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).

Examples

## 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)

igraph

Network Analysis and Visualization

v1.2.10

GPL (>= 2)

Authors

See AUTHORS file.

Initial release