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Childfree adults do not want to have children, making them distinct from parents and other adults without children. However, they are difficult to study because they cannot be identified using conventional data on fertility. We use data from a representative sample in the United States to examine the prevalence, age of decision, and interpersonal warmth judgments by and about childfree adults. Our prevalence estimates suggest that childfree adults are quite common, comprising over one-fifth (21.64%) of the population. Our analysis of age-to-decision suggests that most childfree adults reported that they decided they did not want children early in life. Finally, our analysis of interpersonal warmth suggests asymmetric affective polarization among parents and childfree adults driven primarily by parent's ingroup favoritism. We discuss the implications of these findings for our understanding of childfree adults and for future research on this historically overlooked segment of the population. [ABSTRACT FROM AUTHOR]

Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge's weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge's p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical. [ABSTRACT FROM AUTHOR]

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party. [ABSTRACT FROM AUTHOR]