Homophily is an important social force in many social networks. While homophilous tendencies among positive relations (such as friendship) and heterophilous tendencies in negative tie networks (such as dislike) have been extensively studied, less is known about how such tendencies might become stronger or weaker as a result of past experiences. Focusing on homophilous and heterophilous tendencies between non-Muslim and Muslim students in schools, this paper explores whether, in line with Intergroup Contact Theory, existing friendship connections with outgroup members will reduce their inclination to dislike outgroup peers compared to ingroup peers (i.e., reduce dislike heterophily), and reduce their preferences for ingroup friendships over outgroup friends (i.e., reduce friendship homophily). On the other hand, in line with Attribution Theory and Social Identity Theory, we propose that existing dislike connections with outgroup members are likely to strengthen dislike heterophilous tendencies and friendship homophilous tendencies, thus further reinforcing religious segregation in schools. To test these hypotheses, we apply Stochastic Actor-Oriented Models (SAOMs) to the friendship and dislike networks of 1678 Muslim and non-Muslim students (FIS data) across 17 German school settings where this religious divide is particularly salient. Contrary to what we expected based on Intergroup Contact Theory, our study shows that prior interreligious friendships do not weaken a person’s tendency for friendship homophily. However, it does seem to reduce heterophily in dislike, but only because it makes students more likely to dislike ingroup peers. On the other hand, prior interreligious dislike ties are found to amplify dislike to outgroups, while not affecting friendship homophily. Surprisingly, our study also shows that ingroup friendships reduce dislike for outgroup members. We explore the implications of the findings that friendship homophily appears unaffected by prior interreligious contacts, while dislike heterophily is strengthened by existing negative outgroup ties, but reduced by both outgroup and ingroup friendships.
Infectious pathogens have a high burden in long-term care facilities [LTCFs]. Contact is a primary route of pathogen transmission in these settings, so describing resident-healthcare provider [HCP] contact networks can clarify how routine care assignments may shape opportunities for transmission. In this study, we analyze a large sample of resident-HCP networks in LTCFs across the US. Using multilevel exponential-family random graph models [ERGMs] applied to bipartite graphs, in one of the first applications of these models to healthcare contact data, we investigate network structures associated with resident cohorting based on resident-level characteristics. Methodologically, the fitted pooled bipartite ERGM uses mode-specific, mean-centered log-size interactions and combines Monte Carlo maximum likelihood estimation [MC-MLE] point estimates with stochastic approximation [SA] bootstrap inference, a combination that makes these models practical when the pooled networks differ widely in size. Our analysis suggests that residents with wound care, bedridden status, and ventilator use were associated with more HCP contacts. In contrast, no evidence of cohorting-like HCP sharing was found for measured resident clinical characteristics, including Multi-Drug Resistant Organism [MDRO] status. These results do not estimate intervention effects; rather, they identify contact patterns that infection-control teams and transmission modelers can use to audit resident-HCP assignment practices and to parameterize network-based simulation studies.
Political consumerism has become a widespread form of civic engagement, yet the social mechanisms driving boycotting and buycotting remain insufficiently understood. This study investigates how perceived citizenship norms within individuals’ egocentric networks are associated with their own civic orientations and subsequent participation in consumer activism. Using a national survey of 543 U.S. adults reporting on 1822 social ties, we integrate social learning and social influence theories to examine how network environments cultivate engaged and dutiful citizenship norms. Results show a strong correlation between perceived network norms and individuals’ internalized norms, with engaged citizenship norms significantly predicting both boycott and buycott behaviors. Dutiful norms show no significant association. Structural equation modeling reveals that network influence operates indirectly through internalized engaged norms rather than direct social pressure. The study concludes with practical implications for advocacy groups and public relations practitioners, emphasizing how value-aligned messaging and network-based mobilization strategies can more effectively activate consumer activism.
Ransomware groups pose a threat to both the economy and national security, yet little is known about their structural dynamics. This study analyses the Conti group using leaked Jabber encrypted instant messaging chat data (similar to Messenger) and Relational Event Models to examine how preferential attachment and triadic closure mechanisms shape its communication network. Findings show Conti prioritised efficiency through centralisation but mitigated risks via compartmentalisation, creating a hybrid structure that balances speed and secrecy. This adaptive approach enhances both effectiveness and resilience. The research provides new insights into the evolution of ransomware groups and suggests that disruption strategies should target internal cohesion rather than external infrastructure.
Personal network size is a versatile and fundamental structural network measure, capturing the total number of personal connections or specific subgroups within an individual’s social environment. In standard sociological methods, such as surveys, personal network size is often indirectly inferred from name generators, which are used to list alters from the respondents’ personal networks. Implicitly, a higher count of named alters is assumed to indicate a larger personal network. We explicitly test this common assumptionand address this imprecise association between network size and number of listed alters by comparing the results from two very distinct approaches to measuring personal network size: alter counts derived from name generators and size estimates obtained from the network scale-up method (NSUM). Using survey data from over 300 participants in Spain, we compute these two independent network measures for the same respondents and assess the strength, significance and robustness of their association, including potential question-order effects on personal network size. Our analyses conclude that the alter counts from name generators primarily reflect participants’ response behaviour and task constraints rather than the underlying personal network size. Finally, our findings highlight critical limitations in the use of free name generators as proxies for personal network size.
