backbone: An R Package for extracting the backbone of bipartite projections

Abstract

Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone', an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality.

References in corpus (3)

• Extracting the multiscale backbone of complex weighted networks
• The Babe Ruth Algorithm: a fast, unbiased procedure to randomize presence-absence data matrices with fixed row and column totals
• Transitivity and degree assortativity explained: The bipartite structure of social networks

Cited by in corpus (4)

• Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections
• Meta-validation of bipartite network projections
• Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
• Extracting the signed backbone of intrinsically dense weighted networks