Adolescents' peer interactions strongly influence their school behavior, raising the question of whether parents can still direct adolescents' friendship choices or whether they are mostly on the sidelines. Chinese cultural values emphasize the importance of having "good" friends, raising questions about adolescents' adherence to parental direction of friendships. This study examined friendship dynamics among seventh and eighth graders in central China (n = 1,454, 46.7% girls), focusing on achievement, aggression, and prosocial behavior. Social network analysis revealed that Chinese students tended to select friends who were more similar to them in achievement, aggression, and prosocial behavior. Interestingly, they avoided aggressive peers but were attracted to high-achieving and prosocial peers. Despite parental emphasis on friendship quality, the study found no parental direction in adolescent friendship selection. These findings underscore the central role of peer characteristics in friendship dynamics, while highlighting the limited impact of parental directing on Chinese adolescents' friendship selection.

This chapter gives a critical overview of the models available for analyzing longitudinal social network data, and sketches the two most important model families, the stochastic actor-oriented models and the longitudinal exponential random graph models.

For their economic success, organizations in the social economy are particularly dependent on access to collective resources through interorganizational networks. Because self-organised network governance of an economy is notoriously intransparent, there is the danger that existing societal inequalities get replicated particularly well. This creates a tension with the equality-promoting mission of these organizations. This paper investigates the degree to which the goal of gender equality has been realized in the social economy of Barcelona. By analysing networks of advice-seeking and economic collaboration with exponential random graph models, network mechanisms are analysed to estimate gender-based inequality.

Stochastic models of sociocentric networks were originally developed for testing hypotheses about micro-level dependencies on the basis of empirical network data. Such dependencies lead to phenomena like clustering, hub formation, or network autocorrelation. Due to the complex nature of sociocentric networks, parameter estimates of these models are typically obtained by simulation-based inference. This opens the possibility of re-purposing these models as simulation tools for the study of emergent macro-level phenomena. The combination of fitting micro-level models to empirical data sets and explanation of macro-level outcomes renders these models powerful tools for sociological inquiry into interdependent social systems. In this chapter, the use of stochastic actor-oriented models as generative models for such networked social systems is discussed. This is illustrated with an investigation of the emergence of subgroups in adolescents' friendship networks.

In learning communities, students share their knowledge which might contribute to academic performance. This study disentangles peer selection from influence processes in modelling first-year students’ academic performance after the transition to university. Longitudinal peer network data were obtained from 95 bachelor students at two time points in a social sciences study programme with eight learning communities. Using co-evolution modelling in RSiena, we found that students help each other more often when they are already friends and students who help each other academically are more likely to become friends. The higher a student performs, the more often the student is selected as a friend or as an academic helper and the more often this higher-performing student initiates friendship and academic help relationships. Although learning communities are often implemented to enhance academic performance, we did not find evidence that peer relationships in learning communities influence academic performance.

Knowing driving factors and understanding researcher behaviors from the dynamics of collaborations over time offer some insights, i.e. help funding agencies in designing research grant policies. We present longitudinal network analysis on the observed collaborations through co-authorship over 15 years. Since co-authors possibly influence researchers to have interest changes, by focusing on researchers who could become the influencer, we propose a stochastic actor-oriented model of bipartite (two-mode) author-topic networks from article metadata. Information of scientific fields or topics of article contents, which could represent the interests of researchers, are often unavailable in the metadata. Topic absence issue differentiates this work with other studies on collaboration dynamics from article metadata of title-abstract and author properties. Therefore, our works also include procedures to extract and map clustered keywords as topic substitution of research interests. Then, the next step is to generate panel-waves of co-author networks and bipartite author-topic networks for the longitudinal analysis. The proposed model is used to find the driving factors of co-authoring collaboration with the focus on researcher behaviors in interest changes. This paper investigates the dynamics in an academic social network setting using selected metadata of publicly-available crawled articles in interrelated domains of “natural language processing” and “information extraction”. Based on the evidence of network evolution, researchers have a conformed tendency to co-author behaviors in publishing articles and exploring topics. Our results indicate the processes of selection and influence in forming co-author ties contribute some levels of social pressure to researchers. Our findings also discussed on how the co-author pressure accelerates the changes of interests and behaviors of the researchers.

Longitudinal social network studies can easily suffer from insufficient statistical power. Studies that simultaneously investigate change of network ties and change of nodal attributes (selection and influence studies) are particularly at risk because the number of nodal observations is typically much lower than the number of observed tie variables. This article presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models are to be applied. Two detailed case studies illustrate how statistical power is strongly affected by network size, number of data collection waves, effect sizes, missing data, and participant turnover. These issues should thus be explored in the design phase of longitudinal social network studies.

The aim of this study was to unravel the interrelatedness of friendship and help, and to examine the characteristics of friendship and help networks. The effects of mutual versus one‐sided help relations on friendship initiation and maintenance, and vice versa, were examined. Friendship and help networks were analyzed (N = 953 students; 41 classrooms; Mage = 12.7). The results illustrate that friendship and help networks show some similarities, but only partly overlap and have distinct characteristics. Longitudinal multiplex social network analyses showed that mutual help was important for the maintenance of friendship, but not for the initiation of friendship. Further, particularly mutual friendships provided a context in which help took place. Implications of these findings are discussed.

Prior work has shown that popular peers can set a powerful norm for the valence and salience of aggression in adolescent classrooms, which enhances aggressive friendship processes (selection, maintenance, influence). It is unknown, however, whether popular peers also set a norm for prosocial behavior that can buffer against aggressive friendship processes and stimulate prosocial friendship processes. This study examined the role of prosocial and aggressive popularity norm combinations in prosocial and aggressive friendship processes. Three waves of peer-nominated data were collected in the first- and second year of secondary school (N = 1816 students; 81 classrooms; Mage = 13.06; 50.5% girl). Longitudinal social network analyses indicate that prosocial popularity norms have most power to affect both prosocial and aggressive friendship processes when aggressive popularity norms are non-present. In prosocial classrooms (low aggressive and high prosocial popularity norms), friendship maintenance based on prosocial behavior is enhanced, whereas aggressive friendship processes are largely mitigated. Instead, when aggressive popularity norms are equally strong as prosocial norms (mixed classrooms) or even stronger than prosocial norms (aggressive classrooms), aggression is more important for friendship processes than prosocial behavior. These findings show that the prosocial behavior of popular peers may only buffer against aggressive friendship processes and stimulate prosocial friendship processes if these popular peers (or other popular peers in the classroom) abstain from aggression.

This study addresses the puzzle how high-status bullies in elementary school are able to maintain high status among their classmates despite bullying (some of) them. The dynamic interplay between bullying and status was studied, focusing on how relational bullying affects the creation, dissolution, and maintenance of status attributions, and vice versa. Longitudinal round-robin peer nomination data were obtained from 82 school classes in15 Dutch elementary schools (2055 students; 50% boys) followed over three yearly measurements, starting out in grades 2–5 when students were aged 8-11. An age-dependent effect of bullying on the creation of new status attributions was found. Whereas the youngest group punished bullying by a refusal to attribute status to the bully, this turned into a reward of bullying in the oldest groups. Unexpectedly, high-status bullies seemed to avoid continual bullying of the same victims, pointing to explanations of why their status can persist.