backbone: An R package to extract network backbones

Abstract

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates the `backbone' package for R, which implements methods for extracting the backbone from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the `backbone' package, and future directions for research on network backbone extraction.

References in corpus (7)

• Power-law distributions in empirical data
• A tool for filtering information in complex systems
• Extracting the multiscale backbone of complex weighted networks
• Information filtering in complex weighted networks
• Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections
• Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
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Cited by in corpus (3)

• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• fastball: A fast algorithm to sample bipartite graphs with fixed degree sequences
• Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC) for Backbone Extraction