lp_mce: Link prediction with MCE or ncMCE

In galanisl/LinkPrediction: Link prediction in complex networks

Description

Given a network of interest, it computes the likelihood score of interaction, for all disconnected node pairs, based on the embedding of the network to d-dimensional Euclidean space with centred or non-centred Minimum Curvilinear Embedding (MCE and ncMCE, respectively).

Usage

lp_mce(g, d = 2, centre = FALSE, use_weights = FALSE)

Arguments

g	igraph; The network of interest.
d	integer; The dimension of the embedding space.
centre	logical; Indicates whether the Minimum Curvilinear kernel should be centred or not.
use_weights	logical; Indicates whether edge weights should be used to compute the network's Minimum Curvilinear kernel.

Value

Tibble with the following columns:

nodeA	The ID of a network node.
nodeB	The ID of a network node.
scr	The likelihood score of interaction for the node pair.

Author(s)

Gregorio Alanis-Lobato [e-mail]

References

Cannistraci, C. V. et al. (2013) Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding. Bioinformatics 29:i199-i209

Examples

# Apply the ncMCE link predictor to the Zachary Karate Club 
# network
mce <- lp_mce(g = karate_club, d = 2, centre = FALSE, use_weights = FALSE)

galanisl/LinkPrediction documentation built on May 17, 2019, 12:10 p.m.