backbone: Extract network backbone using the disparity filter

In bibnets: Importing, Constructing, and Exporting Bibliometric Networks

Extract network backbone using the disparity filter

Description

Applies the disparity filter to a weighted edge list. For each edge, it computes an alpha (p-value) from both endpoints and keeps the edge if it is statistically significant from at least one endpoint.

Usage

backbone(edges, alpha = 0.05)

Arguments

edges	A data frame with at least columns from, to, and weight. Must be an undirected edge list (each pair appears once).
alpha	Numeric. Significance threshold in (0, 1). Default 0.05.

Details

The null model asks: given that node i has total strength s_i distributed uniformly across k_i edges, what is the probability that a single edge weight is as large as w_{ij}? The answer is

\alpha_{ij} = \left(1 - \frac{w_{ij}}{s_i}\right)^{k_i - 1}

An edge is retained if \min(\alpha_{ij}, \alpha_{ji}) < \alpha. Nodes with only one edge always have \alpha = 0 and are always kept.

Value

The filtered edge data frame with an added alpha column (the minimum alpha from the two endpoints).

Examples

edges <- data.frame(
  from   = c("A", "A", "A", "B", "C"),
  to     = c("B", "C", "D", "C", "D"),
  weight = c(10,   1,   1,   8,   1)
)
backbone(edges, alpha = 0.05)

bibnets documentation built on June 19, 2026, 1:06 a.m.