Evaluate Link Predictions Against Known Edges — evaluate

Computes AUC-ROC, precision\(@\)k, and average precision for link predictions against a set of known true edges.

Usage

evaluate_links(pred, true_edges, k = c(5L, 10L, 20L))

Arguments

pred: A net_link_prediction object.
true_edges: A data frame with columns from and to, or a binary matrix where 1 indicates a true edge.
k: Integer vector. Values of k for precision\(@\)k. Default: c(5, 10, 20).

Value

A data frame with columns: method, auc, average_precision, and one precision_at_k column per k value.

Examples

set.seed(42)
seqs <- data.frame(
  V1 = sample(LETTERS[1:5], 50, TRUE),
  V2 = sample(LETTERS[1:5], 50, TRUE),
  V3 = sample(LETTERS[1:5], 50, TRUE)
)
net <- build_network(seqs, method = "relative")
pred <- predict_links(net, exclude_existing = FALSE)

# Evaluate: predict the network's own edges
true <- data.frame(from = pred$predictions$from[1:5],
                   to = pred$predictions$to[1:5])
evaluate_links(pred, true)
#>                    method       auc average_precision precision_at_5
#> 1        common_neighbors 0.9933333         1.0000000            1.0
#> 2     resource_allocation 0.9933333         1.0000000            1.0
#> 3             adamic_adar 0.9933333         1.0000000            1.0
#> 4                 jaccard 0.9400000         0.8100000            0.8
#> 5 preferential_attachment 0.6066667         0.5110731            0.4
#> 6                    katz 0.5400000         0.2854257            0.2
#>   precision_at_10 precision_at_20
#> 1             0.5            0.25
#> 2             0.5            0.25
#> 3             0.5            0.25
#> 4             0.5            0.25
#> 5             0.3            0.25
#> 6             0.3            0.25