R igraph manual pages

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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).

Other hierarchical random graph functions: `consensus_tree`, `fit_hrg`, `hrg-methods`, `hrg_tree`, `hrg`, `print.igraphHRGConsensus`, `print.igraphHRG`, `sample_hrg`

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)
```

[Package igraph version 1.2.4.1 Index]