Receiver inference is central to social network analysis because it reveals interaction dynamics such as participation structures, turn-taking patterns, and directed communication ties. Recent advances in large language models (LLMs) have improved the analysis of conversational data; however, their ability to recover sender–receiver structures from multi-party conversations, evaluate uncertainty in these predictions, and assess downstream consequences for relational event modeling remains underexplored. This study addresses this gap by evaluating GPT-4.1 for receiver inference in structured multi-party conversations, prompting the model to predict intended receivers and comparing its outputs with human annotations. We examine multiple prompt designs and benchmark the proposed GPT-4.1-based approach against an open-source LLM and two heuristic baselines: a most-recently-mentioned participant rule and a last-speaker rule. Predicted and annotated sender–receiver relations are converted into relational event history sequences and used to reconstruct directed interaction networks, enabling comparison at both the turn and network levels. The results show that the GPT-4.1-based approach consistently outperforms the open-source model and heuristic baselines, and that tailored prompt design substantially improves receiver inference accuracy. We further assess whether model-based uncertainty estimates reflect prediction correctness by analyzing token-probability traces and self-assessed confidence scores. These confidence measures show moderate correspondence with accuracy, indicating partial calibration. Finally, we apply a relational event model (REM) to examine how LLM-based receiver predictions affect estimated model parameters. Overall, the findings demonstrate the potential of LLMs to enrich relational event history (REH) datasets through receiver inference.
Explaining the endogenous emergence of “gurus” – high-status professionals who are referred to for advice but are unlikely to seek it themselves – in organizations requires an integrated approach. This approach must consider the interplay of individual attributes (e.g., skills and status), structural factors (e.g., informal networks), and organizational contexts (e.g., knowledge-sharing infrastructure and internal competitiveness) from a dynamic perspective. In this study, we develop an agent-based model of advice network formation calibrated on an empirically observed network to identify the factors and processes driving “guru” emergence. Our results show that the emergence of “gurus” depends primarily on status differentials, homophily, and organizational context. In low-competitive settings, a relatively stable proportion of “gurus” emerges once the advice network reaches equilibrium. However, as competitiveness increases, network equilibrium is associated with fewer “gurus”, whose emergence is decoupled from formal hierarchical roles. Contrary to common sense, differences in skills among professionals contribute only marginally to network dynamics and “guru” formation. Overall, our findings suggest a fundamental misalignment between formal hierarchies and informal status dynamics within organizations, which could lead to tension.
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.
