Volumen 19 (2018): Heft 1 (January 2018)

How do civilians select internal displacement destinations during conflict? Existing research emphasizes the value of cascades as a guide to making these difficult decisions. Cascades may involve civilians following people in their social networks (community cascades), people with similar characteristics (co-ethnic cascades), or the crowd in general (herd cascades). Analyses relying upon interview or regression-based methodological approaches face substantial challenges in identifying the prevalence of, and relationship between, each type of cascade. While interview-based approaches can incorporate location characteristics and movement patterns, they struggle with assessing aggregate trends. Meanwhile, regression-based approaches can assess aggregate trends, but they struggle with incorporating location characteristics and movement patterns. Exponential Random Graph Models (ERGMs) that conceive of locations as nodes in a network and movements between those locations as ties can overcome these challenges and assess aggregate trends while incorporating location characteristics and movement patterns. This paper demonstrates the utility of this approach using data from UNHCR on internal displacement in Somalia from 2007-2013. Results reveal that herd cascades only form at high displacement levels, co-ethnic cascades form at medium and high displacement levels, and community cascades form at all displacement levels. Therefore, cascades provide stronger guides for displacement-related decisions as civilians switch from following the crowd in general to following those with similar characteristics to following social ties.

Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL-median heuristic (TMKLMedH) seeks to optimize the same criterion as the RH for deterministic blockmodeling. The TMKLMedH runs much faster than the RH, so many more restarts of the TMKLMedH can be accomplished when the two methods are constrained to the same time limit. Three computational comparisons of RH and TMKLMedH were conducted using both synthetic and real-world networks. In all three comparisons, the superiority of TMKLMedH was unequivocal.

How do sex risk and protection change over the course of a relationship? It is often claimed that protection generally declines over the course of relationships. This 3-year longitudinal study examines 412 new sexual relationships described by 126 adult participants and tests this claim. Analyses identify four relationship trajectories: only 15% of new sex relationships show a declining trajectory of protection; another 12% show only a temporary decline. Population average analyses previously interpreted to show a decline in protection are shown here to be is largely explained by the attrition of the low trust, high protection relationships that creates the association between higher trust and lower protection. The long-term relationships turn out mostly not to have been low trust, high protection relationships at the start. Instead they have mostly always been high trust, low protection relationships. Other proposed theories, notably self-protection and power theories are not supported, while drug use is supported for 15% of the sample. Only trust and secondary partners successfully account for the observed patterns of protection and attrition. Actors seem to be concerned to protect their partners, using more protection with a secondary partner who might provide a risk to the primary partner.

Adolescents who experience a pregnancy often face educational and economical difficulties later in life. One factor that has been found to improve outcomes for pregnant teens is access to social supports. Inopportunely, teen pregnancy presents social obstacles, and cross-sectional analysis has found pregnant teens have fewer friendships than their non-pregnant counterparts. However, longitudinal work has yet to explore network change after a pregnancy. This study uses multiple network modeling techniques to follow the social networks of a group of girls who become pregnant between waves of the Add Health survey. Pregnant teens were found to maintain fewer friendships between time points than peers. Whole school network maps suggest that in some schools teens move to more peripheral network positions following pregnancy. These preliminary findings suggest that the relationship between social network change and pregnancy may vary depending on school environment; future work is needed to better understand how school contexts may change the social outcomes of pregnant girls.