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

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