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This paper aims to understand how a group of academics cite each others' work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals. • We investigate how a group of academics cite each others' work through time considering the co-evolution of a multilevel network. • The co-evolution of a three-mode multilevel network is analysed considering cross-level effects. • New and already available measures for diagnostics are used for statistical models for social networks to identify how micro-mechanisms trigger different structures at the macro level. • Social relationships grounded on scientific collaboration and space proximity based on institutional affiliation are more accurately suited to understand the co-evolution of the networks in a scientific network. [ABSTRACT FROM AUTHOR]

Abstract Introduction MSA is an adult-onset, sporadic, progressive parkinsonian syndrome characterised by the presence of akinesia, cerebellar dysfunction, autonomic failure and pyramidal signs. Annualized-whole-brain atrophy rate (a-WBAR) is an informative way to quantify disease progression. In this longitudinal work we investigate the correlations of a-WBAR with clinical scales for motor impairment, autonomic disability and cognitive decline in MSA and explore how atrophy progresses within the brain. Method Fourty-one MSA patients were studied using Structural Imaging Evaluation with Normalization of Atrophy (SIENA). SIENA is an MRI-based algorithm that quantifies brain tissue volume. Clinical parameters were explored using the 18-item Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale, the Hoehn and Yahr Scale, the Frontal Assessment Battery and the Natural History and Neuroprotection in Parkinson Plus Syndromes scale (sub-items for orthostatic and urinary functions). Results The mean (±SD) age was 60.4 years ± 7.7 and a-WBAR was 1.65% ± 0.9. Demographics and clinical ratings at the time of the first scan were non-significantly associated with a-WBAR. The only exception was the baseline urinary score with a weak but significant association (R2 = 0.15, p = 0.04). Progression of grey matter atrophy was detected in the left superior temporal gyrus, right middle frontal gyrus, right frontopolar region and midbrain. Conclusion Urinary impairment at baseline may help to identify 'fast progressors' in terms of neuronal loss, particularly in the frontal and temporal lobes. Thus, urinary impairment should be recognized as a key target for disease modifying therapeutic interventions in MSA. Highlights • In MSA autonomic dysfunction within the first 3 years from the disease onset is associated with rapid progression to death. • In MSA urinary impairment at baseline may help to select 'fast progressors' in terms of neuronal loss. • Urinary impairment should be recognized as a key target for disease modifying therapeutic interventions in MSA. [ABSTRACT FROM AUTHOR]

Severe public crisis such COVID-19 pandemic entail coordinated communication between politicians and public health agencies. The study explores how and why U.S. politicians share messages from health agencies on social media during COVID. Proposing a multi-theoretical, multi-level (MTML) framework to understand the phenomenon, we draw upon the Advocacy Coalition Framework and Crisis and Emergency Risk Communication theory and conceptualize politicians' public health communication as serving the dual functions of policy and risk communication. With bipartite longitudinal network modeling, our analysis finds a fragmented national message-sharing network deprived of central federal leadership and clustered around state-level actors such as local health agencies and state governors. The politicians' party affiliation and positions on COVID-19 policies significantly impacted whether they would help distribute messages from public health agencies. Health agencies' message features such as expression of certainty and use of analytical words also influenced politicians' message sharing patterns. These findings suggest the pandemic communication is both a policy advocacy and a risk and crisis communication process. This integrated theoretical approach offers explanations of information sharing dynamics between politicians and health agencies, two major information sources for the public. • American politicians selectively share COVID-19 messages from public health agencies. • Longitudinal bipartite network modeling reveals a fragmented sharing network. • Policy sharing is a dual process of advocacy coalition and crisis communication. • Call for a multi-theoretical, multi-level (MTML) approach. [ABSTRACT FROM AUTHOR]

Brain volume loss (BVL) has gained increasing interest for monitoring tissue damage in neurodegenerative diseases including multiple sclerosis (MS). In this longitudinal study, 117 healthy participants (age range 37.3–82.6 years) received at least 2 magnetic resonance imaging examinations. BVL (in %) was determined with the Structural Image Evaluation using Normalisation of Atrophy/FMRIB Software Library and annualized. Mean BVL per year was 0.15%, 0.30%, 0.46%, and 0.61% at ages 45, 55, 65, and 75 years, respectively. The corresponding BVL per year values of the age-dependent 95th percentiles were 0.52%, 0.77%, 1.05% and 1.45%. Pathological BVL can be assumed if an individual BVL per year exceeds these thresholds for a given age. The mean BVL per year determined in this longitudinal study was consistent with results from a cross-sectional study that was published recently. The cut-off for a pathological BVL per year at the age of 45 years (0.52%) was consistent with the cut-off suggested previously to distinguish between physiological and pathological BVL in MS patients. Different cut-off values, however, need to be considered when interpreting BVL assessed in cohorts of higher ages. [ABSTRACT FROM AUTHOR]

Missing data are often problematic when analyzing complete longitudinal social network data. We review approaches for accommodating missing data when analyzing longitudinal network data with stochastic actor-based models. One common practice is to restrict analyses to participants observed at most or all time points, to achieve model convergence. We propose and evaluate an alternative, more inclusive approach to sub-setting and analyzing longitudinal network data, using data from a school friendship network observed at four waves ( N = 694). Compared to standard practices, our approach retained more information from partially observed participants, generated a more representative analytic sample, and led to less biased model estimates for this case study. The implications and potential applications for longitudinal network analysis are discussed. [ABSTRACT FROM AUTHOR]

Immigrants who strongly identify with the host country have more native friends than immigrants with weaker host country identification. However, the mechanisms underlying this correlation are not well understood. Immigrants with strong host country identification might have stronger preferences for native friends, or they might be more often chosen as friends by natives. In turn, having native friends or friends with strong host country identification might increase immigrants’ host country identification. Using longitudinal network data of 18 Dutch school classes, we test these hypotheses with stochastic actor-oriented models. We find that immigrants’ host country identification affects friendship selections of natives but not of immigrants. We find no evidence of social influence processes. [ABSTRACT FROM AUTHOR]

Adolescents’ school-based friendship networks tend to be segregated along ethnic lines. But few studies have examined whether variation in network boundaries affects the degree of ethnic friendship segregation. We use rational-choice theory to argue that ethnic homophily is more pronounced for friendships between classrooms than for those within classrooms. We empirically test this hypothesis using two-wave German panel data ( N = 1258) and stochastic actor-oriented models (RSiena). In line with our theoretical argument, we find that the tendency to form same-ethnic friendships is indeed stronger at the grade level, which translates into stronger ethnic segregation in friendship networks at the grade level than at the classroom level. Implications for research on ethnic segregation in school-based friendship networks are discussed. [ABSTRACT FROM AUTHOR]

Although stochastic actor-based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models. [ABSTRACT FROM AUTHOR]

Highlights: [•] Friends influence adolescents’ homework activities and attention behavior in class. [•] Adolescents who receive less friendship nominations are influenced more. [•] Friends who receive more friendship nominations are not more influential. [•] Unreciprocated friends are not more influential than reciprocated friends. [Copyright &y& Elsevier]

Abstract: Purpose: To examine the relative contribution of weapon carrying of peers, aggression, and victimization to weapon carrying of male and female adolescents over time. Methods: Data were derived from a population-based sample of male (N = 224) and female (N = 244) adolescents followed from grade 10 (M age = 15.5) to grade 11 (M age = 16.5). Peer networks were derived from best friend nominations. Self-reports were used to assess weapon carrying. Aggression and victimization were assessed using both self- and peer-reports. Use of dynamic social network modeling (SIENA) allowed prediction of weapon carrying in grade 11 as a function of weapon carrying of befriended peers, aggression, and victimization in grade 10, while selection processes and structural network effects (reciprocity and transitivity) were controlled for. Results: Peer influence processes accounted for changes in weapon carrying over time. Self-reported victimization decreased weapon carrying 1 year later. Peer-reported victimization increased the likelihood of weapon carrying, particularly for highly aggressive adolescents. Boys were more likely to carry weapons than girls, but the processes associated with weapon carrying did not differ for boys and girls. Conclusions: These findings revealed that, in this population-based sample, weapon carrying of best friends, as well as aggression, contributed to the proliferation of weapons in friendship networks, suggesting processes of peer contagion as well as individual vulnerability to weapon carrying. [Copyright &y& Elsevier]

Abstract: The mutual influence of smoking behavior and friendships in adolescence is studied. It is attempted to disentangle influence and selection processes in reciprocal and non-reciprocal friendships. An actor-based model is described for the co-evolution of friendship networks and smoking behavior. This model considers alternative selection and influence mechanisms, and models continuous-time changes in network and behavior. The data consists of a longitudinal sample of 1326 Finnish adolescents in 11 high schools. Findings suggest that selection as well as influence processes play an important role in adolescent smoking behavior. Selection had a relatively stronger role than influence, in particular when selecting non-reciprocal friends. The strength of both influence and selection processes decreased over time. [Copyright &y& Elsevier]

Professional interaction in the workplace is an indispensable part of professional development. We examined how teachers' teaching collaboration networks within a university department changed throughout an eight-month professional development project and how these networks influenced teachers' observation choices in formative peer observations. Stochastic actor-oriented modeling (SAOM) shows that it was more likely that teachers started to collaborate when they were working on the same floor (propinquity), were more active in attending project meetings, and had more teaching experience. The multiple regression quadratic assignment procedure (MR-QAP) indicates that teachers were more likely to observe colleagues with whom they already collaborated. • Collaboration networks became denser during the professional development project. • Collaboration patterns were positively related to propinquity. • Experienced teachers (more than 5 years) were more likely to collaborate. • Informal interaction outside work was positively related to a collaboration tendency. • Teachers were more likely to observe a colleague with whom they already collaborate. [ABSTRACT FROM AUTHOR]

Abstract: This study aimed at setting up a comprehensive approach on viticultural and olive tree cultivation environmental impact, through soil and landscape change analysis at the years 1965, 1990, 1994 and 2000, both at the catchment and province scales. The study area was the province of Siena, in central Italy. Following a DPSIR approach, the State indicators checked the Impact of agricultural policies (Drivers), which have increased the Pressure of specialized crops, and suggested Response actions. The indicators selected for the catchment scale stressed the consequences of land use change on soil quality, capability and suitability. They were: (1) land use and land cover; (2) soil and water conservation management system, (3) soil hydrological group, (4) land capability; (5) land suitability for wine grape production. At the province level, four original indicators highlighted at the reconnaissance scale the impact of the two crops on soil conservation and management – (6) soil erosion risk, (7) soil management – as well as on visual attractiveness of landscape – (8) slope length of vineyards, (9) crop saturation of the landscape unit and its variation in time. The results showed that, at the catchment level, the twenty-fold increase in specialized viticulture and olive tree cultivation, along with the generalised adoption of the “up and down slope” soil and water conservation system, caused the impairment of soil hydrological group, land capability and suitability for growing quality wine. At the province level, soil erosion risk and soil management difficulty almost doubled in the transition from the traditional mixed to the new specialized tree cultivation. The areas traditionally producing wine protected by appellation of origin (DOCG) determined the distribution of land use and all the other impact indicators. Slope length of vineyards showed that the enlargement of the surface covered by vines preferentially affected lands close to already established DOCG farms. DOCG areas resulted somewhat saturated by vineyards, but not by olive tree groves. The response should foresee the following actions: (i) when planting new olive groves and vineyards, measures should be taken to avoid the complete removal of the subsurface soil B horizon; (ii) new plantations should maintain slope degree of less than 21% in any part of the field; (iii) the shape of new plantations should conform to slope morphology, thus avoiding the creation of long, wide and uniform slopes. The aim should be to limit the length of the fields to less than 150–300m and to adopt or preserve the contour management systems. [Copyright &y& Elsevier]

Interaction is a principle of high-quality course design in online learning. Previous research shows that interaction in Massive Open Online Courses is crucial for learner retention and course completion. Using panel network data of 386 MOOC learners, this study explored the mechanisms that drive learner-learner interaction over time, specifically, the patterns and evolution of learner-learner interaction in a MOOC through a stochastic-actor-oriented model. The results contradicted previous evidence that learners reciprocate open communication (i.e., replies) in discussion forums and tend to interact with those to whom their direct connections reply. The extent to which learners interact with others similar to themselves (i.e., homophily) was not a statistically significant predictor of learner-learner interaction over time. Popularity, as measured by open communication, suggested preferential attachment in MOOC learners. High levels of affective communication received (i.e., likes) reduced open communication over time. Implications for practice are discussed, and future research that analyzes the quality of open communication over time is recommended. • Liking comments is detrimental for learner-learner interaction over time in MOOCs. • Popular learners attract more learner-learner interaction over time in MOOCs indicating the "rich-get-richer" rule. • MOOC learners have no preference to engage in learner-learner interaction over time with those similar to themselves. • MOOC learners' do not reciprocate learner-learner interaction over time. [ABSTRACT FROM AUTHOR]

• Communication networks among trainee teachers become less dense during their training programme. • There is limited boundary-spanning between trainees on university- and school-led programmes. • Friendship is an early-stage predictor in the development of communication networks. • Advice-seeking is a late-stage predictor in the development of communication networks. • Communication around peer-support becomes more mutual among mathematics trainees over time. This study examines the communication networks of 77 pre-service teachers in mathematics and science on university and school-led programmes in England. Communication as an indicator for establishing contact is considered as the starting point of building social capital among peers. Communication, advice-seeking and friendship network data were collected over four time-points during each programme. The descriptive results suggest limited boundary-spanning between the university and school-led programme types. Longitudinal modelling in RSIENA showed that friendship is a significant early-stage predictor of development of communication networks. By the end of the programme advice-seeking becomes a significant predictor of network development. Neither interpersonal trust, nor perceived self-efficacy were found to be consistent predictors of change in pre-service teacher communication networks. [ABSTRACT FROM AUTHOR]

Combining choice modelling with social network analysis, we show how the stochastic actor-oriented model for the co-evolution of networks and behavior (SAOM) can be used as a powerful statistical framework to empirically analyze network-related choices. We discuss the underlying assumptions of SAOMs and show that they can be interpreted to represent a random utility maximization model (RUM). Network-related choices pertain both to decisions to engage in (or disengage from) specific social relationships and decisions to adapt behavior to that of social contacts. We demonstrate the usefulness of SAOM for the choice modelling community. We further illustrate how SAOM can be used to study network-related choices by providing an exemplary empirical analysis. [ABSTRACT FROM AUTHOR]