Longitudinal Network Research Articles

Do alcohol interventions affect peers who do not receive the intervention? Modeling treatment contagion effects via simulations of adolescent social networks.

Adolescents' drinking is influenced by their friends' drinking. However, it is unclear whether individually-targeted alcohol interventions reduce drinking in the friends of individuals who receive the intervention. This study used simulations of drinking in simulated longitudinal social networks to test whether individually-targeted alcohol interventions may be expected to spread to non-targeted individuals. Stochastic actor-based models simulated longitudinal social networks where changes in drinking and friendships were modeled using parameters from a meta-analysis of high school 10th grade social networks. Social influence (i.e., how much one's friends' drinking affects their own drinking) and social selection (i.e., how much one's drinking affects who they select as friends) were manipulated at several levels. At the midpoint of each simulation, a randomly-selected heavy-drinking individual was experimentally assigned to an intervention (changing their drinking status to non-drinking) or a control condition (no change in drinking status) and the drinking statuses of that individual's friends were recorded at the end of the simulation. Friends of individuals who received the intervention significantly reduced their drinking, with higher reductions occurring in networks with greater social influence. However, all effect sizes were small (e.g., average per-friend reduction of .07 on a 5-point drinking scale). Individually-targeted alcohol interventions may have small effects on reducing the drinking of non-targeted adolescents, with social influence being a mechanism that drives such effects. Due to small effect sizes, many adolescents may need to receive alcohol interventions to produce measurable effects on drinking outcomes for non-targeted individuals. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Policy learning as complex contagion: how social networks shape organizational beliefs in forest-based climate change mitigation

Policy learning can alter the perceptions of both the seriousness and the causes of a policy problem, thus also altering the perceived need to do something about the problem. This then allows for the informed weighing of different policy options. Taking a social network perspective, we argue that the role of social influence as a driver of policy learning has been overlooked in the literature. Network research has shown that normatively laden belief change is likely to occur through complex contagion—a process in which an actor receives social reinforcement from more than one contact in its social network. We test the applicability of this idea to policy learning using node-level network regression models on a unique longitudinal policy network survey dataset concerning the Reducing Deforestation and Forest Degradation (REDD+) initiative in Brazil, Indonesia, and Vietnam. We find that network connections explain policy learning in Indonesia and Vietnam, where the policy subsystems are collaborative, but not in Brazil, where the level of conflict is higher and the subsystem is more established. The results suggest that policy learning is more likely to result from social influence and complex contagion in collaborative than in conflictual settings.

Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada

• Montreal Pavement Dataset includes four distress types and marking, clean images. • Model with Batch Normalization and lower rate of Dropout has the best performance. • The Convolutional Neural Network model classifies pavement distress types efficiently. • Data was collected using low-cost high-resolution cameras mounted on any vehicle. Pavement distresses, including cracking and disintegration, deteriorate road user’s comfort, damage vehicles, increase evasive maneuvers, and increase emissions. Transportation agencies spend a significant portion of their budget to monitor and maintain road pavements. Pavement distress can be identified through manual surveys, i.e., visual inspections of pavement images captured by an inspection vehicle. To reduce manual inspection costs, research and industry have moved quietly towards the development and implementation of automated road surface monitoring systems. Considering the latest research developments, the objective of this work is to propose and evaluate a methodology for automated detection and classification of pavement distress types using Convolutional Neural Networks (CNN) and a low-cost video data collection strategy. In this work, pavement distress types are categorized as linear or longitudinal crack, network crack, fatigue crack or pothole, patch, and pavement marking. The models are trained and tested based on an image dataset collected from Montreal’s road pavements. A sensitivity analysis is carried on for evaluating different regularization scenarios and data generation strategies especially scaling and partitioning the input image. The detection rate and classification accuracy of the proposed approach with the trained CNN model reaches 83.8% over the test set, which is promising compared with the literature. More specifically, the F1-scores for “ pothole ”, “ patch ”, “ marking ”, “ crack-linear ” and “ crack-network ” classes are 0.808, 0.802, 0.860, 0.796, and 0.813, respectively. However, by merging linear and network crack classes, the F1-score over the merged class increases to 0.916.

An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios

Studies on self-driving transport vehicles have focused on longitudinal and lateral driving strategies in automated structured road scenarios. In this study, a double parallel network (DP-Net) combined with longitudinal and lateral strategy networks is constructed for self-driving transport vehicles in structured road scenarios, which is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, in feature extraction and perception, a preprocessing module is introduced that can ensure the effective extraction of visual information under complex illumination. Then, a parallel CNN sub-network is designed that is based on multifeature fusion to ensure better autonomous driving strategies. Meanwhile, a parallel LSTM sub-network is designed, which uses vehicle kinematic features as physical constraints to improve the prediction accuracy for steering angle and speed. The Udacity Challenge II dataset is used as the training set with the proposed DP-Net input requirements. Finally, for the proposed DP-Net, the root mean square error (RMSE) is used as the loss function, the mean absolute error (MAE) is used as the metric, and Adam is used as the optimization method. Compared with competing models such as PilotNet, CgNet, and E2E multimodal multitask network, the proposed DP-Net is more robust in handling complex illumination. The RMSE and MAE values for predicting the steering angle of the E2E multimodal multitask network are 0.0584 and 0.0163 rad, respectively; for the proposed DP-Net, those values are 0.0107 and 0.0054 rad, i.e., 81.7% and 66.9% lower, respectively. In addition, the proposed DP-Net also has higher accuracy in speed prediction. Upon testing the collected SYSU Campus dataset, good predictions are also obtained. These results should provide significant guidance for using a DP-Net to deploy multi-axle transport vehicles.

Peer influence in adolescent drinking behavior: A meta-analysis of stochastic actor-based modeling studies.

ObjectivesTo measure the effects of peer influence and peer selection on drinking behavior in adolescence through a rigorous statistical approach designed to unravel these interrelated processes.MethodsWe conducted systematic searches of electronic databases, thesis collections and conference proceedings to identify studies that used longitudinal network design and stochastic actor-oriented modeling to analyze drinking behavior in adolescents. Parameter estimates collected from individual studies were analyzed using multilevel random-effects models.ResultsWe identified 26 articles eligible for meta-analysis. Meta-analyses for different specifications of the peer influence effect were conducted separately. The peer influence effect was positive for every specification: for average similarity (avSim) mean log odds ratio was 1.27 with 95% confidence interval [0.04; 2.49]; for total similarity (totSim) 0.46 (95% CI = [0.44; 0.48]), and for average alter (avAlt) 0.70 (95% CI = [-0.01; 1.41]). The peer selection effect (simX) was also positive: 0.46 (95% CI = [0.28; 0.63]). Conversion log odds ratio values to Cohen’s d gives estimates from 0.25 to 0.70, which is considered as medium to large effect.ConclusionsAdvances in methodology for social network analysis have made it possible to accurately estimate peer influence effects free from peer selection effects. More research is necessary to clarify the roles of age, gender, and individual susceptibility on the changing behavior of adolescents under the influence of their peers. Understanding the effects of peer influence should inform practitioners and policy makers to design and deliver more effective prevention programs.

On Their Own? The Work-Related Social Interactions and Turnover of New Teachers

Work-related social interactions are key to the effectiveness and retention of new teachers, yet little research has examined changes in these networks over time. Using longitudinal social network data from 14 elementary schools, this study explores new teachers’ instructional advice interactions with their colleagues. Results suggest that new teachers are relatively peripheral to their schools’ networks, maintain similar advice-seeking patterns as their early careers progress, and that new teachers’ advice-seeking predicts leaving their schools or teaching the following year. These findings have implications for efforts to support new teachers’ collegial interactions and retention.

Network investigation of eating disorder symptoms and positive and negative affect in a clinical eating disorder sample.

Growing literature suggests that emotions influence the maintenance of eating disorder (ED) symptoms. However, most research has studied the relationship between ED symptoms and affect broadly (i.e., negative affect [NA], positive affect [PA]), rather than examining models comprised of multiple specific affective states (e.g., upset, proud). The current study (N = 196 individuals with EDs) used network analysis to examine the most interconnected (i.e., central) NA and PA states in EDs and test the complex associations between specific NA, PA, and ED symptoms. We estimated two networks: one with affective states only and another with affective states and ED symptoms. Feeling distressed, afraid, attentive, and determined were the most central symptoms in the affect-only network. ED symptoms related to overvaluation of weight and shape, including affect-based ED symptoms (i.e., guilt about eating), were central in the network of affect and ED symptoms. Guilt about eating and shame were central bridge symptoms across affect and ED symptom clusters, meaning that they were each strongly connected across clusters, and may represent important pathways among affect and ED symptoms. Limitations include the cross-sectional and between-person nature of these analyses, from which we cannot derive causal or within-persons processes. Clinical interventions that target central and bridge symptoms (e.g., fear, shame) may disrupt the reinforcing cycle of NA in EDs that may contribute to ED behaviors. Future research should examine relationships among affective states and ED symptoms in longitudinal and intraindividual network models to develop more effective treatments for EDs.

Visualising the past to plan the future: a network analysis of the largest European dermatology conference.

The annual conference of the European Academy of Dermatology and Venereology is one the largest dermatology conferences worldwide. Network analysis can be used for in-depth insight into trending topics and underlying trends at the congress. Network analysis was employed to assess the entirety of the submitted abstracts to the congress in 2019. The data were processed, analysed, and visualised using easy-to-understand network graphs. Topics were then compared to their respective global burden (Disease Adjusted Life Years [DALYs]) and the number of respective publications on PubMed in the year 2018. Overall, 1,280 lecture titles and 1,941 poster titles were included in the final analysis. The most frequently used terms were "patients" (n = 473), "treatment" (n = 301), and "psoriasis" (n = 335). Relative to DALYs, "psoriasis" (+21.9%) among others, was rather over-represented, while "fungal skin diseases" (-7.6%) and "urticaria" (-6.4%) were under-represented. Compared to the relative number of PubMed publications in 2018, "psoriasis" (+20.3%), "acne" (+7.9%), and "alopecia" (+3.1%) were over-represented, while "melanoma" (-22.5%), "dermatitis" (-4.2%) and "pruritus" (-3.4%) were rather under-represented. The network analysis showed that the congress was a patient and therapy-centred event. An explanation for the particular focus on chronic inflammatory skin diseases and melanoma would be the introduction of new therapies at the congress. To delineate trends over time, a longitudinal network analysis including several congresses should be conducted and could be used to determine additional topics to be included in future events.

Meditation as panacea: A longitudinal semantic network analysis of meditation coverage in campus newspapers from 1997–2018

Objective Media framing of health issues reflects public opinion and impacts readers’ perceptions and behavior. This study examines how meditation — a recommended stress coping strategy for college students — is framed in campus newspapers from 1997-2018. Participants A total of 494 articles were analyzed. Methods Semantic network analysis was used to automatically detect frames and the longitudinal trend. Results Five major frames emerged: (1) building a meditation community within a campus community, (2) meditation benefits, (3) yoga for enhancing mind and body awareness, (4) meditation techniques, and (5) secularizing meditation on campus. There is a shift in coverage from interest in religion to secular views of health benefits throughout the years. Discussions of adverse effects that have emerged from the literature were entirely absent. Conclusions The trend of secularizing meditation practices on college campuses is evident. Emphasizing the techniques and benefits could encourage participation and build a learning community.

The role of prosocial attitudes and academic achievement in peer networks in higher education

After the transition to university, students need to build a new peer network, which helps them to adapt to university life. This study investigated to what extent students’ prosocial attitudes and academic achievement facilitate the embeddedness in friendship and help-seeking networks, while taking structural network characteristics into account. Participants were 95 first-year bachelor’s degree students and were part of learning communities consisting of 12 students at a university in the Netherlands. Measures included student-reports of prosocial attitudes, peer nominations of friendship and help-seeking networks, and officially registered grades (GPA). Longitudinal social network analysis, stochastic actor-based modeling with the package RSiena, revealed that both students’ own prosocial attitudes and achievement played a role in their friendship formation, whereas only students’ own achievement made the formation of their help-seeking relationships more likely. When students were friends, it was more likely that they approached each other for help and vice versa. Similarity in achievement level contributed to relationship formation in friendship and help-seeking networks. Overall, the results underscore the importance of both student’ prosocial attitudes and achievement for their social adjustment (i.e., making friends) and only achievement for their academic adjustment (i.e., seeking help) during the first year of university within the context of small-scale teaching.

Moderators of Friend Selection and Influence in Relation to Adolescent Alcohol Use.

Friendships form an important context in which adolescents initiate and establish alcohol use patterns, but not all adolescents may be equally affected by this context. Therefore, this study tests whether parenting practices (i.e., parental discipline, parental knowledge, unsupervised time with peers) and individual beliefs (i.e., alcohol descriptive norms, positive social expectations, moral approval of alcohol use) moderate friend selection and influence around alcohol use. Stochastic actor-based models were used to analyze longitudinal social network and survey data from 12,335 adolescents (aged 11 to 17, 51.3% female) who were participating in the PROSPER project. A separate model was estimated for each moderating variable. Adolescents who reported consistent parental discipline, less unsupervised time with peers, higher descriptive alcohol use norms, and less positive social expectations about alcohol use were less likely to select alcohol-using friends. Those who reported consistent parental discipline, better parental knowledge, lower descriptive alcohol use norms, and less positive social expectations were more influenced by their friends' level of alcohol use. Thus, adolescents with these characteristics whose friends frequently use alcohol are at greater risk whereas those whose friends do not use alcohol are at lower risk of using alcohol. The findings show that, although selection and influence processes are connected, they may function in different ways for different groups of adolescents. For some adolescents, it is particularly important to prevent them from selecting alcohol-using friends, because they are more susceptible to influence from such friends. These peer network dynamics might explain how proximal outcomes targeted by many prevention programs (i.e., parenting practices and individual beliefs) translate into changes in alcohol use.

Shyness and Psychological Maladjustment in Chinese Adolescents: Selection and Influence Processes in Friendship Networks

Few studies have been conducted to examine the role shyness plays in friendship selection and influences processes, particularly how befriending shy peers affects individual's psychological adjustment. To address these gaps, this study investigated the selection, de-selection, same behavior influence effects of shyness in the friendship network in Chinese adolescents using the longitudinal social network analysis. It also explored the possible pathways that transmit indirect influences of friends' shyness to individual's psychological maladjustments (i.e., loneliness and depressive symptoms). A sample of adolescents (N = 1254, 48.4% girls, Mage = 13.20 years at Time 1) were followed for three years in middle schools. The results showed that adolescents tended to befriend those who had similar shyness level during T1-T2 interval, but the friendships between shy friends were more likely to terminate during T2-T3 interval. Moreover, adolescents were found to become more similar with their friends in shyness over time. The study also found that friends' shyness at T1 would influence individual's loneliness at T3 via 1) increasing friends' loneliness at T2 or 2) increasing individual's shyness level at T2. Those two pathways, however, were not found for depressive symptoms. These findings indicate that shyness plays an important role in the friendship formation and dissolution and befriending shy peers may have implications for adolescents' development of shyness and loneliness.

Evolution of Collaborative Governance in the 2015, 2016, and 2018 Myanmar Flood Disaster Responses: A Longitudinal Approach to a Network Analysis

In disaster response, collaboration facilitates interactions among actors, such as the government, the military, nongovernmental organizations, and civil society organizations. This study examined the longitudinal changes in collaborative governance in Myanmar’s disaster responses based on cases of flooding in 2015, 2016, and 2018. To examine the mechanisms underlying this dynamic network formation, the collaborative ties of the actors involved in search and rescue activities were converted into longitudinal relational data sets, and the evolution of collaborative governance was analyzed by relying on the assumptions of social capital, transaction cost, homophily, and resource dependency theories and using a longitudinal social network analysis method. The findings show that the collaborative networks of search and rescue processes in disaster response evolved and changed over time according to the hypothesized patterns of strong, weak, and preferential tie formations. The study also revealed that the collaborative governance system assumes the form of a hierarchy rather than a generalized exchange, and the actors’ reliance on military organizations is not obvious due to the emerging alternative non-military actors and diverse local actors observed in the cases.

A comparison of dyadic and social network assessments of peer influence

The present study compares two methods for assessing peer influence: the longitudinal actor–partner interdependence model (L-APIM) and the longitudinal social network analysis (L-SNA) Model. The data were drawn from 1,995 (49% girls and 51% boys) third grade students (Mage = 9.68 years). From this sample, L-APIM (n = 206 indistinguishable dyads and n = 187 distinguishable dyads) and L-SNA (n = 1,024 total network members) subsamples were created. Students completed peer nominations and objective assessments of mathematical reasoning in the spring of the third and fourth grades. Patterns of statistical significance differed across analyses. Stable distinguishable and indistinguishable L-APIM dyadic analyses identified reciprocated friend influence such that friends with similar levels of mathematical reasoning influenced one another and friends with higher math reasoning influenced friends with lower math reasoning. L-SNA models with an influence parameter (i.e., average reciprocated alter) comparable to that assessed in L-APIM analyses failed to detect influence effects. Influence effects did emerge, however, with the addition of another, different social network influence parameter (i.e., average alter influence effect). The diverging results may be attributed to differences in the sensitivity of the analyses, their ability to account for structural confounds with selection and influence, the samples included in the analyses, and the relative strength of influence in reciprocated best as opposed to other friendships.

Building an Equitable Surgical Training Pipeline: Leadership Exposure for the Advancement of Gender and Underrepresented Minority Equity in Surgery (LEAGUES)

Underrepresented minority (URM) medical students face many educational challenges. Barriers include lack of equitable representation, scarce mentorship, and the effects of systemic racism. For students interested in diversity and health equity, perceptions of surgical culture may discourage pursuing surgical specialties. We describe a national pilot for a novel surgical pipeline program, Leadership Exposure for the Advancement of Gender and Underrepresented Minority Equity in Surgery (LEAGUES), which utilizes early exposure, mentorship, and community building to empower URM students in pursuit of academic surgical careers. A 4-week virtual program included pairing students with faculty research mentors, virtual skills sessions, and seminars on leadership, advocacy, and career development. Participants underwent semi-structured interviews before and after participation, assessing experiences with mentorship and research, interest in surgery, career aspirations, and perceived barriers to career goals. Department of Surgery, Michigan Medicine, Ann Arbor, Michigan. Rising second-year medical students. All 3 participants were Latinx; 2 were first-generation college students. Participants had no surgical mentorship and limited research exposure, citing a desire to learn research methodology, connect with mentors, and build towards a career working with underserved communities as motivating factors for participation. Perceived barriers to a surgical career included surgical culture, burnout, and lack of research expertise or academic network necessary for success. At completion of the program, participants described several themes: (1) new positive perspective on academic surgical culture, (2) interest and confidence in research, (3) hope for improving health disparities, (4) networking and longitudinal mentorship connections contributing to a sense of surgical community, and (5) eagerness to share resources with colleagues at their home institutions. LEAGUES program participants acquired tools and motivation to pursue careers in surgery, and established valuable longitudinal network and mentor relationships. LEAGUES is a novel model for national surgical pipeline programs.

Technological and analytical advancements in intergroup contact research

AbstractThe prolific expansion of intergroup contact research has established that intergroup interactions are tightly linked to social integration. In this review, recent technological and statistical innovations with the potential to advance this body of research are presented. First, concerns over the validity of longitudinal models are discussed before innovative analytical techniques are introduced that explore change over time. Next, intensive repeated measure designs, such as experience sampling approaches, are introduced as opportunities to investigate the day‐to‐day lives of individuals. Virtual reality technology is then presented as another means to examine naturalistic contact experiences in the laboratory, offering researchers an unrivaled capacity to induce uncommon contact experiences. Finally, we propose that additional sources of contextual data, such as competing media messages, could extend these models in innovative ways by accounting for the time and place surrounding intergroup contact. Similarly, longitudinal social network analysis can provide additional contextual information by considering the broader network environment in which contact occurs. We describe these innovations with the intention of spurring future research that will advance our understanding of how intergroup contact can be used to improve our societies. Thus, we conclude with a discussion on how to bridge divides between researchers and practitioners.

The role of perspective‐taking and low social class prejudice on cross‐ethnic friendship formation

Cross-ethnic friendships offer a unique opportunity for improving intergroup relations and reduce prejudice, yet ethnic segregation of friendship networks is often seen as a major obstacle to the integration of immigrant students in educational contexts. This article examines the role of perspective-taking abilities and prejudice towards low social class peers on the probability of cross-ethnic friendships in a sample of 242 students from five multicultural classrooms in Chile (Mage = 12.3; SD = 0.69, 45% girls). It was expected that students who reported high levels of perspective-taking abilities and low levels of prejudice towards low social class peers were more likely to form and maintain cross-ethnic friendships. Longitudinal network analysis (RSiena) was used to examine these hypotheses, confirming the role of both variables in fostering (and reducing) friendships among Chilean and immigrant adolescents. Results are discussed in light of an intersectional framework between social class and ethnicity. Implications for social-emotional interventions in multicultural educational contexts are also discussed.

Fan Ties and Friendships: A Longitudinal Network Study of Division III Sports on Campus

The purpose of this longitudinal actor-based network study is to examine the evolution of sport fan ties and friendship ties on Division III campuses. Using two years of network data from a cohort of new students at a Division III institution, a SIENA model is developed to empirically test the co-evolution of fan ties and friendship ties. Grounded in student development, sport consumer behavior, and network theories, the overarching goal of this study is to explore the causal effect of fan ties on friendship ties among new students. To ascertain the value of Division III sports on campus, the authors explore the role of sports on campus in promoting friendships for new students based on the strong theoretical tradition from student development highlighting the salience of peer relationship in student success and retention. Accordingly, theoretical implications related to sport fan networks are considered within practical discussion of Division III sports and retention.

Growing pains in local food systems: a longitudinal social network analysis on local food marketing in Baltimore County, Maryland and Chester County, Pennsylvania

Local food systems are growing, and little is known about how the constellation of farms and markets change over time. We trace the evolution of two local food systems (Baltimore County, Maryland and Chester County, Pennsylvania) over six years, including a dataset of over 2690 market connections (edges) between 1520 locations (nodes). Longitudinal social network analysis reveals how the architecture, actor network centrality, magnitude, and spatiality of these supply chains shifted during the 2012–2018 time period. Our findings demonstrate that, despite growth in the number of farmers’ markets, grocery stores, farms and restaurants in both counties, each local food system also experienced high turnover rates. Over 80% of the market connections changed during the study period. Farms, farmers’ markets, and grocery stores showed a 40–50% ‘survival’ rate, indicating their role in sustaining local food systems over longer time periods. Other actors, such as restaurants, had a much higher turnover rate within the network. Both food systems became more close-knit and consolidated as the center of gravity for both local food systems pulled away from urban areas toward rural farmland. Evidence of both growth and decay within local food systems provides a new understanding of the social networks behind local food markets.

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.