Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC) for Backbone Extraction

Abstract

It is common to use the projection of a bipartite network to measure a unipartite network of interest. For example, scientific collaboration networks are often measured using a co-authorship network, which is the projection of a bipartite author-paper network. Caution is required when interpreting the edge weights that appear in such projections. However, backbone models offer a solution by providing a formal statistical method for evaluating when an edge in a projection is statistically significantly strong. In this paper, we propose an extension to the existing Stochastic Degree Sequence Model (SDSM) that allows the null model to include edge constraints (EC) such as prohibited edges. We demonstrate the new SDSM-EC in toy data and empirical data on young children's' play interactions, illustrating how it correctly omits noisy edges from the backbone.

References in corpus (5)

• Randomizing bipartite networks: the case of the World Trade Web
• Inferring monopartite projections of bipartite networks: an entropy-based approach
• backbone: An R package to extract network backbones
• Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections
• fastball: A fast algorithm to sample bipartite graphs with fixed degree sequences