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Bipartite projections (e.g., event co-attendance) are often used to measure unipartite networks of interest (e.g., social interaction). Backbone extraction models can be useful for reducing the noise inherent in bipartite projections. However, these models typically assume that the bipartite edges (e.g., who attended which event) are unconstrained, which may not be true in practice (e.g., a person cannot attend an event held prior to their birth). We illustrate the importance of correctly modeling such edge constraints when extracting backbones, using both synthetic data that varies the number and type of constraints, and empirical data on children’s play groups. We find that failing to impose relevant constraints when the data contain constrained edges can result in the extraction of an inaccurate backbone. Therefore, we recommend that when bipartite data contain constrained edges, backbones be extracted using a model such as the Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC). 

Early childhood is an important developmental period for network formation. However, the observational methods used for measuring young children’s networks present challenges for capturing both positive and negative ties. To overcome these challenges, we explored the use of a bipartite projection backbone model for inferring both negative and positive ties from observational data of children’s play. Using observational data collected in one 3-year-old (N = 17) and one 4-year-old (N = 18) preschool classroom, we examined whether patterns of homophily, triadic closure, and balance in networks inferred using this method matched theoretical and empirical expectations from the early childhood literature. Consistent with this literature, we found that signed networks inferred using a backbone model exhibited gender homophily in positive ties and gender heterophily in negative ties. Additionally, networks inferred from social play exhibited more closed and balanced triads than networks inferred from parallel play. These findings offer evidence of the validity of bipartite projection backbone models for inferring signed networks from preschoolers’ observed play. 

Children and adolescents interact in peer groups, which are known to influence a range of psychological and behavioral outcomes. In developmental psychology and related disciplines, social cognitive mapping (SCM), as implemented with the SCM 4.0 software, is the most commonly used method for identifying peer groups from peer report data. However, in a series of four studies, we demonstrate that SCM has an unacceptably high risk of false positives. Specifically, we show that SCM will identify peer groups even when applied to random data. We introduce backbone extraction and community detection as one promising alternative to SCM, and offer several recommendations for researchers seeking to identify peer groups from peer report data.