lp_leig: Link prediction with Laplacian Eigenmaps

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 Laplacian Eigenmaps.

Usage

lp_leig(g, d = 2, use_weights = FALSE)

Arguments

g	igraph; The network of interest.
d	integer; The dimension of the embedding space.
use_weights	logical; Indicates whether edge weights should be used to compute the network's Laplacian matrix.

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

Belkin, M. and Niyogi, P. (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neur. In. 14:585-591

Examples

# Apply the Laplacian Eigenmaps link predictor to the Zachary Karate Club 
# network
leig <- lp_leig(g = karate_club, d = 2, use_weights = FALSE)

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