Longitudinal Network Data Research Articles

On the spurious effect of intergroup friendship on outgroup attitudes in schools: The role of social influence and the positive impact of exposure to outgroup peers.

Promoting intergroup friendships in schools is regarded as a powerful strategy for improving outgroup attitudes. However, stochastic actor-oriented modelling (SAOM) studies have often revealed no association between intergroup friendship and outgroup attitudes. I investigated whether SAOM studies reported no effect of intergroup friendship on outgroup attitudes, what was responsible for this surprising finding and whether exposure to outgroup peers was positively related to outgroup attitudes. The meta-analysis of SAOM studies confirms no association between intergroup friendship and outgroup attitudes. Examining longitudinal social network data of 2700 German students, I found that a positive link between intergroup friendship and outgroup attitudes appeared only when I did not control for social influence. This indicates that intergroup friendship did not automatically improve outgroup attitudes. Instead, the development of outgroup attitudes among students depended on social influence and, thus, the quality of outgroup attitudes among their outgroup friends. Exposure to outgroup peers was, however, positively associated with outgroup attitudes. These findings reframe intergroup contact theory by suggesting that intergroup friendship is not essential for improving outgroup attitudes. Rather, social influence and exposure to outgroup peers could be key factors shaping outgroup attitudes.

Network Position Affects Social Status in Early Adolescence

ABSTRACT Does position in the friendship network affect social status in early adolescence? Previous research shows that friendship relations are not equivalent to popularity hierarchies, but favorable positions in the friendship network should allow students to gain status. We use four waves of longitudinal network data and dynamic panel models with fixed effects to estimate the impact of network position on social status. Degree centrality and brokerage opportunity both exert large impacts on status, even compared against the effect of prior status. The results suggest future research on friendship across social identities, as well as network structure and hierarchy in adolescent social systems.

A Resource-Acquisition Perspective: Examining the Effects of Downsizing on Work-Related Relationships and Performance

Today’s corporations increasingly use downsizing as a change strategy to improve organizational performance. Although downsizing and employee networks have garnered attention from both scholars and practitioners, few studies have investigated the influence of downsizing on the temporal dynamics of communication networks among surviving employees or how changes in communication patterns in organizations affect performance. To study how downsizing affects layoff survivors—extending Conservation of Resources theory to longitudinal network and employee-performance data—we examine the impact of downsizing on both the behavioral and structural consequences in an organizational network and test whether temporal changes in network members’ degree centrality predict how employees who survive a downsizing event perform in their jobs. Results indicate that, during the period immediately following a downsizing event, survivors’ new tie-seeking behavior results in gains in degree centrality when compared with degree centrality before the downsizing or after organization routines stabilize. Moreover, survivors with lower pre-downsizing degree centrality achieved greater gains in degree centrality than those with higher degree centrality. We find that substantial gains in degree centrality are positively related to post-downsizing performance. Efforts to regain degree centrality are abandoned during the stabilization period, and changes in degree centrality are no longer positively related to post-downsizing performance. Our results demonstrate that dynamic changes in degree centrality during disruption and stabilization periods following a downsizing event have differential effects on work-related relationships and performance. We discuss the theoretical and managerial implications of these results and suggest future research directions.

Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network

We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by 40%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$40\\%$$\\end{document} and 18%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$18\\%$$\\end{document}, respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.

Going online: Peer entrepreneur networks in a startup accelerator before and during the COVID-19 pandemic

A key value proposition of startup accelerators is the creation of social networks among participating entrepreneurs. The formation of these so-called “peer entrepreneur networks” is assumed to be strengthened by physical proximity within the accelerator, which facilitates the creation of trust and opportunities for informal, and often serendipitous, interactions. However, in response to the global spread of COVID-19, accelerators abruptly shifted their programs online, thereby allowing a rare opportunity to test the veracity of the assumption that physical proximity drives social connectivity. To understand how this shift affected peer entrepreneur networks, we compare longitudinal network data of two consecutive cohorts of the same accelerator: one offline-before, and one online-during, the COVID-19 pandemic. Drawing from the literature on physical proximity and interaction ritual theory, we show that in the online (compared to the offline) program, peer entrepreneur networks became less dense, entrepreneurs reached fewer peers via indirect connections, and clustering increased. We discuss contributions to theory on peer entrepreneur networks and startup accelerators.

Micro Effects on Macro Structure in Social Networks

How do individuals’ network selection decisions create unique network structures? Despite broad sociological interest in the micro-level social interactions that create macro-level network structure, few methods are available to statistically evaluate micro-macro relationships in social networks. This study introduces a general methodological framework for testing the effect of (micro) network selection processes, such as homophily, reciprocity, or preferential attachment, on unique (macro) network structures, such as segregation, clustering, or brokerage. The approach uses estimates from a statistical network model to decompose the contributions of each parameter to a node, subgraph, or global network statistic specified by the researcher. A flexible parametric algorithm is introduced to estimate variances, confidence intervals, and p values. Prior micro-macro network methods can be regarded as special cases of the general framework. Extensions to hypothetical network interventions, joint parameter tests, and longitudinal and multilevel network data are discussed. An example is provided analyzing the micro foundations of political segregation in a crime policy collaboration network.

The Social Dynamics of Fun: Can Fun Youth Bring Peers Together and Positively Influence Their Friends?

There is a critical gap in our understanding of how peer relationships contribute to positive youth development. To address this gap the current study uses longitudinal social network data to examine if fun youth were socially desirable, inclusive of peers, and positive agents of social influence during the transition to adolescence. Participants were 210 students (47% female; Mage = 11.55 years at the outset) from 8 classes in 3 schools in a small Lithuanian city. Each child received a fun score consisting of nominations from classmates as "someone who is fun to be around". Participants also nominated up to five classmates as friends. Fun students received more friendship nominations than their peers and they maintained higher levels of social desirability over time. Fun youth appeared to be inclusive of their peers in that fun youth did not appear to have preferences to befriend only those like themselves. The friends of fun youth were positively influenced such that they were perceived to be more fun over time. The findings suggests that fun youth may promote positive social change within peer groups.

Creating an interdisciplinary collaborative network of scholars in child maltreatment prevention: A network analysis of the Doris Duke Fellowships for the Promotion of Child Well-Being

BackgroundChild maltreatment is a complex problem requiring interdisciplinary collaborative research to generate innovative solutions. The Doris Duke Fellowships for the Promotion of Child Well-Being were designed to identify and nurture emerging scholars committed to child maltreatment prevention and create a supportive interdisciplinary learning network. ObjectiveThis paper examines connectivity within the collaborative network created by the fellowships program using longitudinal social network data. Participants and SettingParticipants were 120 individuals selected as Doris Duke Fellows during their doctoral training at universities in the United States. MethodsFellows completed annual, voluntary web-based surveys to assess their interactions with other fellows during the past year. Social network analysis methods were used to assess the strength and quality of the learning network over a four-year period. ResultsAcross four years of data, there were increases in the number of connections, proportion of cross-cohort connections, and proportion of interdisciplinary connections. Network analyses showed a highly connected network consisting of primarily medium- and high-quality connections between fellows from different disciplines. The number of scientific journal articles authored by two or more fellows grew substantially over time. ConclusionsFindings indicate the collaborative network created by the fellowships program is growing and strengthening over time. The new Child Well-Being Research Network extends the fellowships network to a broader group of scholars and professionals to advance diversity, equity, inclusion, and justice in the field of child well-being research.

Network patterns of university-industry collaboration: A case study of the chemical sciences in Australia

University–industry (U–I) collaboration takes on many forms, from research services, teaching and training, to curiosity-led research. In the chemical industries, academic chemists generate new knowledge, address novel problems faced by industry, and train the future workforce in cutting-edge methods. In this study, we examine the dynamic structures of collaborative research contracts and grants between academic and industry partners over a 5-year period within a research-intensive Australian university. We reconstruct internal contract data provided by a university research office as records of its collaborations into a complex relational database that links researchers to research projects. We then structure this complex relational data as a two-mode network of researcher-project collaborations for utilisation with Social Network Analysis (SNA)—a relational methodology ideally suited to relational data. Specifically, we use a stochastic actor-oriented model (SAOM), a statistical network model for longitudinal two-mode network data. Although the dataset is complicated, we manage to replicate it exactly using a very parsimonious and relatable network model. Results indicate that as academics gain experience, they become more involved in direct research contracts with industry, and in research projects more generally. Further, more senior academics are involved in projects involving both industry partners and other academic partners of any level. While more experienced academics are also less likely to repeat collaborations with the same colleagues, there is a more general tendency in these collaborations, regardless of academic seniority or industry engagement, for prior collaborations to predict future collaborations. We discuss implications for industry and academics.

Does academic freedom matter for global student mobility? Results from longitudinal network data 2009–2017

Academic freedom and global student mobility are both topics high on the scientific and political agenda. However, the relationship between transnational student mobility and academic freedom in national higher education systems has not yet been investigated cross-nationally. This study intends to answer the question on how a countries’ level of academic freedom impacts on its’ attractiveness as a study destination. We analyse this connection from a network analytic perspective. While our data covers 167 countries as receivers and senders, our network analysis takes the attributes of countries and their relationship to each other into account in order to estimate the net effect of academic freedom ties in the network of global student mobility. We expect global student mobility to be directed from countries with low levels of academic freedom to countries with high levels of academic freedom. At the same time, academic freedom might be an attractive characteristic of a country to retain students in its domestic higher education system.

Our Friends Keep Us Together: The Stability of Adolescents' Cross-Race Friendships.

Substantive racial integration depends on both access to cross-race friendship opportunities (demographic integration) and the development of stable and rewarding social relations (social integration). Yet, we know little about the relative stability of cross-race friendship nominations over time. Cross-race friendships are also experienced within social contexts, where other individual, dyadic, and contextual factors may simultaneously affect whether such ties persist. Based on longitudinal network data on over 2,000 students in multiple communities, we test whether cross-race friendships are more or less stable than same-race friendships. We find that cross-race friendships at first glance appear less likely to persist than same-race friendships, but cross-race ties become no less stable than same-race ties after accounting for other social factors, including reciprocity and shared friends. This pattern suggests a threshold process where strong, socially recognized ties embedded among peers face less threat to maintaining friendship stability.

The dynamics of intergenerational closure and family networks of social cohesion.

We investigate the correlation of ties among school-children's parents with violence in schools, and two mechanisms of intergenerational closure (IC). Coleman described ties among parents of befriended children as IC. Until now, IC indicated social capital in schools and neighborhoods, but existing evidence is rather ambiguous and does not utilize network data. According to "top-down." IC, children establish network ties because of the acquaintance among their parents. "Bottom-up" IC implies that children make friends first and then their parents get involved. We use longitudinal social network data from k = 10 school classes and N = 238 adolescents and disentangle the two different dynamics of IC by applying Bayesian stochastic actor-oriented models (SAOMs) for network evolution. SAOMs show positive "top-down" and "bottom-up" effects on IC, with the latter being considerably stronger.

Determinants of Alliance Formation and Dissolution Among International Health Organizations: The Influence of Homophily and Institutional Power in Affinity Communication Networks

Guided by institutional theory, this study examines how homophily and institutional power influence the tie formation and dissolution of interorganizational collaboration networks. The analysis focuses on longitudinal network data collected from 174 international non-governmental organizations and inter-governmental organizations (IGOs) whose mission and main activities revolved around HIV/AIDS and other related health topics such as substance use, alcohol use, and smoking. This study conceptualizes collaboration as an affinity communication network and uses Separable Temporal Exponential Random Graph Modeling to investigate the effects of organizational and sector level attributes. The results reveal the important role that homophily plays in terms of geolocation and topic alignment. Furthermore, the results show IGOs’ role in driving these global partnerships. Implications on how to sustain global health alliances are discussed.

A tutorial for modeling the evolution of network dynamics for multiple groups

Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.

Contagion, Confounding, and Causality: Confronting the Three C’s of Observational Political Networks Research

AbstractContagion across various types of connections is a central process in the study of many political phenomena (e.g., democratization, civil conflict, and voter turnout). Over the last decade, the methodological literature addressing the challenges in causally identifying contagion in networks has exploded. In one of the foundational works in this literature, Shalizi and Thomas (2011, Sociological Methods and Research 40, 211–239.) propose a permutation test for contagion in longitudinal network data that is not confounded by selection (e.g., homophily). We illustrate the properties of this test via simulation. We assess its statistical power under various conditions of the data, including the nature of the contagion, the structure of the network through which contagion occurs, and the number of time periods included in the data. We then apply this test to an example domain that is commonly considered in the context of observational research on contagion—the international spread of democracy. We find evidence of international contagion of democracy. We conclude with a discussion of the practical applicability of the Shalizi and Thomas test to the study of contagion in political networks.

Joining Policy Forums Together to Develop Ki-no-Eki, a Community Currency System for Forest Management in Japan: Dynamics of Policy Communication Networks

In some mountainous areas of Japan, the Ki-no-Eki system, in which wood is collected to thin the forest and is exchanged for community currency, has been specifically designed and implemented as a solution to current and emerging forest governance issues. This study aimed to capture the evolutionary processes of a complete communication network consisting of organizations that joined policy forums to help develop the Ki-no-Eki system. A total of 26 policy forums were held from 2011 to 2019 to discuss the adoption and implementation of the Ki-no-Eki system across Japan, and coattendance and the resultant policy discourses among 62 participating Ki-no-Eki organizations in these forums were regarded as dynamic communication network processes. We analyzed how policy communication networks formed and evolved to understand the underlying network dynamics driven by not only endogenous network processes—bonding and bridging social capital—but also exogenous effects defined by actors’ attributes. We employed the stochastic actor-oriented model for network dynamics to manage the collected longitudinal undirected network data. We found (i) the emergence of bonding social capital and (ii) homophilic and heterophilic connections in communication networks, which provided insightful explanations of the driving forces of social cohesion among Ki-no-Eki organizations engaged in forest management in Japan.

Peer Entrepreneur Networks in a Startup Accelerator Before and During the Covid-19 Pandemic

In response to the global spread of COVID-19, startup accelerators have shifted all activities online. To understand how this shift affects peer entrepreneur networks – a key constituent of accelerators value proposition to participating entrepreneurs – we draw on longitudinal network data from two editions of the same startup accelerator program before and during the COVID-19 pandemic. Based on interaction ritual theory and the physical proximity principle, we hypothesize and test how online acceleration limits social connectivity. We find that in the online, compared to the offline cohort, networks became less dense, entrepreneurs reached fewer peers via indirect connections; and clustering increased. We highlight the impact of physical distancing on entrepreneurship and discuss contributions to theory on peer networks and startup acceleration.

The development of peer networks and academic performance in learning communities in higher education

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.

A more unstable resting-state functional network in cognitively declining multiple sclerosis.

Cognitive impairment is common in people with multiple sclerosis and strongly affects their daily functioning. Reports have linked disturbed cognitive functioning in multiple sclerosis to changes in the organization of the functional network. In a healthy brain, communication between brain regions and which network a region belongs to is continuously and dynamically adapted to enable adequate cognitive function. However, this dynamic network adaptation has not been investigated in multiple sclerosis, and longitudinal network data remain particularly rare. Therefore, the aim of this study was to longitudinally identify patterns of dynamic network reconfigurations that are related to the worsening of cognitive decline in multiple sclerosis. Resting-state functional MRI and cognitive scores (expanded Brief Repeatable Battery of Neuropsychological tests) were acquired in 230 patients with multiple sclerosis and 59 matched healthy controls, at baseline (mean disease duration: 15 years) and at 5-year follow-up. A sliding-window approach was used for functional MRI analyses, where brain regions were dynamically assigned to one of seven literature-based subnetworks. Dynamic reconfigurations of subnetworks were characterized using measures of promiscuity (number of subnetworks switched to), flexibility (number of switches), cohesion (mutual switches) and disjointedness (independent switches). Cross-sectional differences between cognitive groups and longitudinal changes were assessed, as well as relations with structural damage and performance on specific cognitive domains. At baseline, 23% of patients were cognitively impaired (≥2/7 domains Z < −2) and 18% were mildly impaired (≥2/7 domains Z < −1.5). Longitudinally, 28% of patients declined over time (0.25 yearly change on ≥2/7 domains based on reliable change index). Cognitively impaired patients displayed more dynamic network reconfigurations across the whole brain compared with cognitively preserved patients and controls, i.e. showing higher promiscuity (P = 0.047), flexibility (P = 0.008) and cohesion (P = 0.008). Over time, cognitively declining patients showed a further increase in cohesion (P = 0.004), which was not seen in stable patients (P = 0.544). More cohesion was related to more severe structural damage (average r = 0.166, P = 0.015) and worse verbal memory (r = −0.156, P = 0.022), information processing speed (r = −0.202, P = 0.003) and working memory (r = −0.163, P = 0.017). Cognitively impaired multiple sclerosis patients exhibited a more unstable network reconfiguration compared to preserved patients, i.e. brain regions switched between subnetworks more often, which was related to structural damage. This shift to more unstable network reconfigurations was also demonstrated longitudinally in patients that showed cognitive decline only. These results indicate the potential relevance of a progressive destabilization of network topology for understanding cognitive decline in multiple sclerosis.

Adolescent Body Mass Index and Exposure to Peers with Overweight and Obesity: A Structural Equation Model Approach to Longitudinal Network Data.

Purpose: Considerable evidence has shown that social networks influence a wide variety of health behaviors. This study investigates whether having friends with overweight/obesity in one's social network (network exposure) can predict changes in body mass index (BMI) throughout high school in a diverse urban population of students. Methods: Racially and ethnically diverse students from five high schools in Los Angeles County were surveyed at four time points throughout high school from 2010 to 2013 (N = 2091). Surveys included questions on students' social networks, demographics, and health-related information. BMI and weight categories were calculated for all students who provided height and weight information (∼50%). A latent growth curve model was used to assess the growth trajectory of BMI and the time-varying effect of network exposure to friends with overweight/obesity while controlling for demographic covariates. Results: Hispanic students had a significantly higher initial BMI compared with non-Hispanic students (p < 0.01). There was a significant positive slope for time on BMI growth (p < 0.01). Greater personal network exposure to friends with overweight/obesity was associated with a significant 0.65-point average increase in BMI (p < 0.05) at the first follow-up time point (T2) and a significant 0.62-point average increase in BMI (p < 0.01) at the last follow-up (T4) while controlling for covariates. Conclusions: Using structural equation modeling to understand the relationship between BMI and social networks, we found that increased network exposure to peers with overweight/obesity is associated with higher individual BMI, demonstrating that friendships may influence adolescents' weight status over time.