predict_links: Predict Missing or Future Links in a Network

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Predict Missing or Future Links in a Network

Description

Computes link prediction scores for all node pairs using one or more structural similarity methods. Accepts netobject, mcml, cograph_network, or a raw weight matrix.

All methods are fully vectorized using matrix operations — no loops. Supports both weighted and binary adjacency, directed and undirected networks.

Usage

predict_links(
  x,
  methods = c("common_neighbors", "resource_allocation", "adamic_adar", "jaccard",
    "preferential_attachment", "katz"),
  weighted = TRUE,
  top_n = NULL,
  exclude_existing = TRUE,
  include_self = FALSE,
  katz_damping = NULL
)

Arguments

x	A netobject, mcml, cograph_network, or numeric square matrix.
methods	Character vector. One or more of: "common_neighbors", "resource_allocation", "adamic_adar", "jaccard", "preferential_attachment", "katz". Default: all six methods.
weighted	Logical. If TRUE, use the weight matrix directly instead of binarizing. Default: TRUE.
top_n	Integer or NULL. Return only the top N predictions per method. Default: NULL (all pairs).
exclude_existing	Logical. If TRUE, exclude node pairs that already have an edge. Default: TRUE.
include_self	Logical. If TRUE, include self-loop predictions. Default: FALSE.
katz_damping	Numeric or NULL. Attenuation factor for Katz index. If NULL, auto-computed as 0.9 / spectral_radius(A). Default: NULL.

Details

Methods

common_neighbors

Number of shared neighbors. For directed graphs, sums shared out-neighbors and shared in-neighbors. Vectorized as A %*% t(A) + t(A) %*% A.

resource_allocation

Zhou et al. (2009). Like common neighbors but weights each shared neighbor z by 1/degree(z). Penalizes hubs, rewards rare shared connections.

adamic_adar

Adamic & Adar (2003). Like resource allocation but weights by 1/log(degree(z)). Less aggressive penalty than RA.

jaccard

Ratio of shared neighbors to total neighbors. For directed graphs, computed on combined (out+in) neighbor sets.

preferential_attachment

Product of source out-degree and target in-degree. Captures the "rich-get-richer" effect.

katz

Katz (1953). Weighted sum of all paths between nodes, exponentially damped by path length. Computed via matrix inversion: (I - beta * A)^{-1} - I. Captures global structure.

Value

An object of class "net_link_prediction" containing:

predictions

Data frame with columns: from, to, method, score, rank. Sorted by score (descending) within each method.

scores

Named list of score matrices (one per method).

methods

Character vector of methods used.

nodes

Character vector of node names.

directed

Logical.

weighted

Logical.

n_nodes

Integer.

n_existing

Integer. Number of existing edges.

References

Liben-Nowell, D. & Kleinberg, J. (2007). The link-prediction problem for social networks. JASIST, 58(7), 1019–1031.

Zhou, T., Lu, L. & Zhang, Y.-C. (2009). Network topology and link prediction. European Physical Journal B, 71, 623–630.

Adamic, L. A. & Adar, E. (2003). Friends and neighbors on the Web. Social Networks, 25(3), 211–230.

Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39–43.

See Also

evaluate_links for prediction evaluation, build_network for network estimation.

Examples

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)
print(pred)
summary(pred)

Nestimate documentation built on April 20, 2026, 5:06 p.m.