predict_edges: Predict edges based on a hierarchical random graph model

In igraph: Network Analysis and Visualization

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() 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

## 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)

igraph documentation built on Oct. 20, 2024, 1:06 a.m.