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

TL;DR

This paper introduces an extension to the Stochastic Degree Sequence Model that incorporates edge constraints, improving the extraction of significant edges in network backbones, demonstrated on toy and real-world data.

Contribution

The paper presents a novel SDSM-EC model that accounts for edge constraints, enhancing backbone extraction accuracy over existing models.

Findings

SDSM-EC effectively omits noisy edges in toy data.

Application to children's play interactions shows improved backbone extraction.

Model demonstrates robustness in empirical network analysis.

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.