Stochastic Actor-oriented Models Research Articles

How academic achievement spreads: The role of distinct social networks in academic performance diffusion.

Behavior diffusion through social networks is a key social process. It may be guided by various factors such as network topology, type of propagated behavior, and the strength of network connections. In this paper, we claim that the type of social interactions is also an important ingredient of behavioral diffusion. We examine the spread of academic achievements of first-year undergraduate students through friendship and study assistance networks, applying stochastic actor-oriented modeling. We show that informal social connections transmit performance while instrumental connections do not. The results highlight the importance of friendship in educational environments and contribute to debates on the behavior spread in social networks.

International Students’ Cross-Cultural Adjustment: Social Selection or Social Influence?

International education provides students with an opportunity to develop new social networks while they fit in to the new culture. In a three-wave longitudinal study, we investigated how social networks and psychological adjustment coevolve within a group of international students enrolled in a coursework degree at the tertiary level. Using the Stochastic Actor-Oriented Model (SAOM), we identified the occurrences of social selection based on the levels of psychological and sociocultural adjustment. More specifically, students tended to deselect classmates who were dissimilar in their level of psychological adjustment and to befriend those who differed in their levels of sociocultural adjustment. In contrast, little evidence was found to suggest that features of social networks influenced students’ adjustment. Potential applications of this new method to future acculturation research are suggested.

Neither influence nor selection: Examining co-evolution of political orientation and social networks in the NetSense and NetHealth studies.

Political orientation is one of the most important and consequential individual attributes studied by social scientists. Yet, we know relatively little about the temporal evolution of political orientation, especially at periods in the life course during which individuals are forming new social relationships and transitioning to new relational contexts. Here we use Stochastic Actor-Oriented models (SAOMs) to examine the co-evolution of political orientation and social networks using two feature-rich, temporal network datasets from samples of students making the transition to college at the University of Notre Dame (i.e. the NetSense and NetHealth studies). Overall, we find a great deal of stability in political orientation, with a slight tendency for the 2011 NetSense study participants to become more conservative during their first four semesters in college, but not the 2015 NetHealth study participants. Partisanship is the best predictor of changes in political orientation, with students who identify or vote as Republicans becoming more conservative over time. Neither network influence nor selection processes seem to be driving observed changes. During this formative period, relatively stable identities such as party affiliation predict changes in political orientation independently of local network dynamics, selection processes, socio-demographic traits, and dispositional factors.

You’ve Got a Friend: Examining Board Interlock Formation After Financial Restatements

We investigate the impact of financial restatements as critical events that influence board interlock formation among Fortune 500 firms during the 2009–2013 period. Our empirical study is based on a longitudinal analysis of tie formation while accounting for dynamic changes in the behavior and characteristics of network nodes using stochastic actor-oriented models. We find that firms facing financial restatements experience disruption in network ties. However, social status helps mitigate these effects, and restating firms build new ties through socially embedded processes, such as reciprocity and transitivity. Our work contributes to the understanding of how interorganizational relationships are altered as a result of financial restatement events.

Co-Orientation of Media Use: Studying Selection and Influence Processes in Social Networks to Link Micro Behavior of TV and YouTube Use to Meso-Level Structures

ABSTRACT Social interactions regarding media content are omnipresent and highlight that media use is closely linked to social life. However, surprisingly little is known about the related social dynamics and their consequences for media use and social relations. This study addresses this topic based on co-orientation theory and investigates the social dynamics of TV and YouTube use among adolescents. A three-wave panel survey among 336 pupils enables us to disentangle which effect the friendship network has on individual media use (RQ1) and which effect media use has on the development and maintenance of friendship ties (RQ2). Based on a multi-level approach of dynamic social network analysis and applying stochastic actor-oriented modeling, we find empirical evidence for processes of social influence regarding TV programs and YouTube channels. No support is found for the hypothesized social selection processes. Along with these empirical insights, the research design demonstrates how the micro-level of individual behavior can be linked with the meso-level of social groups. Furthermore, the insights can be used as a basis to understand larger-scale phenomena emerging on the macro-level of societies and media markets such as audience fragmentation and long-tail audience distributions across media contents.

Short-term and long-term effects of a social network intervention on friendships among university students

Informal social relations, such as friendships, are crucial for the well-being and success of students at all levels of education. Network interventions can aim at providing contact opportunities in school settings to prevent the social isolation of individuals and facilitate integration between otherwise segregated social groups. We investigate the short-term and long-term effects of one specific network intervention in an undergraduate cohort freshly admitted to an engineering department ({bf{N}}{boldsymbol{=}}{bf{226}}). In this intervention, we randomly assigned students into small groups at an introduction event two months prior to their first day at university. The groups were designed to increase mixed-gender contact opportunities. Two months after the intervention, we find a higher rate of friendships, common friends, and mixed-gender friendships in pairs of students who were assigned to the same group than in pairs from different groups (short-term effects). These effects gradually diminish over the first academic year (long-term effects). Using stochastic actor-oriented models, we investigate the long-term trajectory of the intervention effects, while considering alternative network processes, such as reciprocity, transitivity, homophily, and popularity. The results suggest that even though the induced friendship ties are less stable than other friendships, they may serve as early seeds for complex social network processes. Our study shows that simple network interventions can have a pronounced short-term effect and indirect long-term effects on the evolution and structure of student communities.

Targets' Social Relationships as Antecedents and Consequences of Workplace Bullying: A Social Network Perspective.

Research on workplace bullying has largely focused on individual and organizational factors that place individuals in a vulnerable position. Although theorists have highlighted social aspects of workplace bullying and its antecedents, the role of individuals' social relations with other members of their organization has rarely been examined empirically. Drawing on insights from social network research and research on social rejection, the purpose of this study was to examine the relationship between exposure to bullying and employees' informal social relationships (here: friendships; negative relationships) with other members of their organization. Data from two waves of surveys among 249 employees in eight organizations in Finland were analyzed using stochastic actor-oriented modeling. We found that employees' centrality (i.e., the number of their relationships) had no effect on exposure to bullying. However, exposure to bullying affected targets' perceptions of their relationships with colleagues: employees who had experienced bullying subsequently reported significantly more friendship relationships, but not significantly more negative relationships, suggesting that aggressive or antisocial responses may be more muted in field settings than in experimental settings. Our study contributes to research on workplace bullying by providing a more detailed understanding of the relationship between workplace bullying and employees' social relations, and by offering insights about the consequences of workplace bullying for targets' social relations.

Social Influences on Obesity: Current Knowledge, Emerging Methods, and Directions for Future Research and Practice.

Individuals tend to be socially connected with those of similar weight and obesity status. To inform future research and intervention development, we reviewed recent literature examining social influences on weight with a focus on mechanisms of social influence, populations studied, and emerging analytical methods. Social networks appear to influence weight gain and weight loss. It remains unclear what underlying mechanisms (e.g., social norms, social comparison, behavioral modeling) drive this relationship. Stochastic actor-oriented modeling is an important method in the field, but other work has leveraged natural experiment or randomized designs to study social influence. Future networks and obesity research should examine social influence mechanisms, focus on diverse populations across the life course, and carefully consider how to adequately control for competing factors of social selection and physical environments.

Choice modelling in social networks using stochastic actor-oriented models

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.

Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes

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.

Network capital, exploitative and exploratory innovations——from the perspective of network dynamics

Taking the patent data in the field of nano energy from 2000 to 2018 as an example, this paper divides organizations’ network capital into four dimensions (i.e., knowledge combinatorial capacity, knowledge stocks, technological distance and network efficiency) and uses Stochastic Actor-Oriented Models to test the relationships between the four dimensions of network capital, exploratory innovation, and exploitative innovation. We found that exploratory innovation and exploitative innovation could be supportive of each other. Organizations’ knowledge combinatorial capacity has a positive effect on exploitative innovation, but a negative effect on exploratory innovation. Both knowledge stocks and technological distance have inverted U-shaped relationships with exploratory innovation and exploitative innovation. Network efficiency has a positive impact on exploratory innovation and an inverted U-shaped relationship with exploitative innovation.

The Meeting of Minds: Forging Social and Intellectual Networks within Universities

How are social and intellectual relations structured and given shape within research universities? To answer these questions, we test to what extent various theoretically predicted processes explain the dynamics of academics’ networks of collaboration and shared language use in a unique longitudinal data set (1994 to 2005) of 2,631 faculty at a large private American university. Using the latest advances in stochastic actor-oriented models (in RSiena) and text analysis, we found that social and intellectual relations are clustered and centralized on bridging faculty who form a broader interdisciplinary hub of research in the university, and that, over time, this hub disseminated its style of (interdisciplinary) research to other faculty. These networks are shaped by selection based on age, gender, race, and academic rank as well as the coevolution of social and intellectual relations over time. Clear differences emerge in science, technology, engineering, and mathematics (STEM) fields and are strongly driven by structural mechanisms of clustering and centralization, whereas non-STEM fields (social sciences and humanities) are strongly driven by personal preferences of faculty members.

A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexus

Abstract Multi-location knowledge-intensive firms span their value chains and thus their locations across space. Increased globalization alters the spatial configuration of such networks of knowledge creation. Longitudinal social network analysis allows detecting temporal changes in the arrangement of nodes and edges in the network and resulting changes in the overall structure. We use this approach to study for Germany the spatio-temporal dynamics of knowledge-intensive services firms – advanced producer services (APS) – in the years between 2009 and 2019. Multi-location APS firms are considered as vanguard of spatial structural change and thus lending to study their location choice behavior. A common approach is to analyze a one-mode intercity network where cities are the nodes. We take a different approach and include the firms’ perspectives. We work directly with the original data structure of a two-mode network including cities and firms as two node sets and we apply stochastic actor-oriented models for network dynamics. Results show that the spatio-temporal dynamics are characterized by both agglomeration and network economies. On a local scale, APS firms continue their location expansion over time and concentrate in agglomerations where many other APS firms and a greater availability of workforce are present. Simultaneously, they also choose new locations in agglomerations further apart from their present locations. On a supra-local scale, the network grows denser over time. Agglomerations that are attractive for APS firms in 2009 become even more attractive in 2019. Our analysis contributes to an understanding of how interactions amongst cities and firms on a local scale give rise to the empirically observed network patterns on a supra-local scale.

Farm size shapes friend choice amongst rice producers in China: Some evidence for the theory of network ecology

Multiple dynamics jointly determine who we befriend, however, researchers have failed to systematically assess which processes matter most under different circumstances. Here I draw on observations around how the demands of paddy rice cultivation shape social interaction to demonstrate that the relative importance of reciprocity, transitivity and generalised exchange to who rice producers choose as friends varies with the amount of agricultural land under their control. In doing so, I use unique data on farm size and friendship amongst 4713 rice-growing smallholders in 162 rural villages in Jiangxi, China along with a new technique for measuring the relative importance of effects in Stochastic Actor-Oriented Models. In line with the micro-level component of the recently advanced “theory of network ecology”, results suggest that features of an individual’s proximal environment can powerfully moderate the relative expression of network-formation mechanisms such that for some individuals, a dynamic may be expected to hold substantial sway over the process of choosing social contacts and, for others, no sway at all.

Informal relations and communication about work-related problems in two multilingual crews

Although multilingual crews are common in international merchant shipping, there is concern that they may have negative effects on informal relations and communication on board, with implications for wellbeing and safety. This study examines how diversity in crew members' first language and their proficiency in English and other languages affect informal relations (friendship) and communication about work-related problems (discussing problems; making suggestions; pointing out problems) on board. Data came from longitudinal surveys on two cargo ships, and were analysed using stochastic actor-oriented modeling. Findings indicated that first language and language proficiency had different effects. Having the same first language increased the likelihood of friendship, which, in turn, increased the likelihood of discussing problems, making suggestions and pointing out problems. Proficiency in English increased the likelihood of discussing problems, which in turn increased the likelihood of making suggestions and pointing out problems. Implications for promoting informal relations and open communication in multilingual crews are discussed.

Sociocultural mechanisms of conflict: Combining topic and stochastic actor-oriented models in an analysis of Afghanistan, 1979–2001

How do sociocultural dynamics shape conflict? We develop a relational understanding of how social relations, culture, and conflict are interwoven. Using this framework, we examine how combatants’ associations with cultural elements affect the interpersonal relationships underlying conflict dynamics, as well as how these relationships engender associations to cultural elements. To do so, we first introduce a novel analytical approach that synthesizes computational textual analysis and stochastic actor-oriented models of longitudinal networks. We then use our approach to analyze a two-level socio-semantic graph representing both the cultural domain and social relationships of prominent militants operating in one Afghan province, Balkh, between 1979 and 2001. Our results indicate that militants’ interpersonal comradeships rely, in part, on their connections to cultural elements and relative power. Comradeship, in turn, fosters militants’ connections to cultural elements. We conclude by discussing how conflict studies can continue to build on insights from cultural sociology, as well as how cultural sociology and socio-semantic network research can benefit from further engaging conflict studies and developing our analytical approach. We also highlight provisional insights into endogenous mechanisms of conflict resolution and cultural change.

The Emergence and Stability of Groups in Social Networks

An important puzzle in social network research is to explain how macro-level structures emerge from micro-level network processes. Explaining the emergence and stability of structural groups in social networks is particularly difficult for two reasons. First, because groups are characterized both by high connectedness within (group cohesion) and lack of connectedness between them (group boundaries). Second, because a large number of theoretical micro-level network processes contribute to their emergence. We argue that traditional social network theories that are concerned with the evolution of positive relations (forces of attraction) are not sufficient to explain the emergence of groups because they lack mechanisms explaining the emergence of group boundaries. Models that additionally account for the evolution of negative ties (forces of repulsion) may be better suited to explain the emergence and stability of groups. We build a theoretical model and illustrate its usefulness by fitting stochastic actor-oriented models (SAOMs) to empirical data of co-evolving networks of friendship and dislike among 479 secondary-school students. The SAOMs include a number of newly developed effects expressing the co-evolution between positive and negative ties. We then simulate networks from the estimated models to explore the micro-macro link. We find that a model that considers forces of attraction and repulsion simultaneously is better at explaining groups in social networks. In the long run, however, the empirically informed simulations generate networks that are too stylized to be realistic, raising further questions about model degeneracy and time heterogeneity of group processes.

Modeling Dynamics of Project-Based Collaborative Networks for BIM Implementation in the Construction Industry: Empirical Study in Hong Kong

AbstractUsing the method of stochastic actor-oriented models (SAOM) and longitudinal data on building information modeling (BIM)-based construction projects undertaken in Hong Kong during 2008–2017...

An Empirical Comparison of Statistical Models for Longitudinal Elementary Teachers’ Advice Networks

이 연구의 목적은 초등교원의 종단 조언 네트워크에 관한 통계 모형을 경험적으로 비교하는 것이다. 두 가지 연구 문제는 첫째, 종단 지수임의 그래프 모형 (TERGM) 과 확률론적 행위자 기반 모형 (SAOM) 사이의 결과의 유사점과 차이점은 무엇인가? 둘째, 교원의 종단 조언 네트워크에 적합한 통계 모형은 무엇인가? 이를 위해 2018학년도 1학기 초등 교원의 조언 네트워크 (n = 54)와 교원 소진을 4회 반복 측정하였고, 이러한 자료를 종단 지수임의 그래프 모형과 확률론적 행위자 기반 모형으로 분석하였다. 그 분석결과, 종단 지수임의 그래프 모형과 확률론적 행위자 기반 모형 사이의 유사성은 교원의 조언 네트워크 변화에 전이성 효과, 동일 학년 유사성 효과, 공식 지도자의 대중성 효과 및 개인 성취감 결여의 활동성 효과이었다. 반면에 두 모형의 차이점은 교원의 조언 네트워크 변화에 호혜성 효과와 교원 총 경력의 대중성 효과이었다. 이러한 차이는 주로 시간에 대한 관점 (이산 대 연속성)과 시간 종속성에 대한 처리방법 (장기종속 대 1차 마르코프 과정)에 기인한다고 볼 수 있다. 이러한 결과를 토대로, 교원의 종단 조언 네트워크 자료에 적합한 통계 모형을 제안했다.The purpose of this study was to empirically compare statistical models for longitudinal elementary teachers’ advice networks. The two research questions were: first, what are the similarities and differences between the results of the temporal exponential random graph model and the stochastic actor-oriented model? Second, which statistical models are appropriate for longitudinal teachers’ advice networks? To this end, we measured teachers’ advice networks (n=54) and teachers’ burnout four times in one elementary school in 2018, which we analyzed by temporal exponential random graph models and stochastic actor-oriented models. Results showed that similarities between two types of models included the transitivity effect, the grade-level homophily effect, the alter effect of formal leader on the change of teachers’ advice networks, and the reduced feelings of personal accomplishment ego effect on the change of teachers’ advice network. Meanwhile, differences between the two models were the reciprocity effect and the total teaching experience effect on the change of teachers’ advice networks. These differences are mainly due to the viewpoint about time (discrete versus continuous) and the treatment of temporal dependencies (longer dependence versus a first-order Markov process). Based on these results, we suggest appropriate statistical models for longitudinal teachers’ advice network data.

The coevolution of trade agreements and investment treaties: Some evidence from network analysis

Regional trade agreements (RTAs) and bilateral investment treaties (BITs) are expected to promote trade and investment relationships. One critical feature of such agreements is the network, so the multiplex coevolution of RTAs and BITs should be captured by the dynamics of their two networks. Although many studies have examined the roles of RTAs and BITs, most studies do not account for crucial network properties. This study explores how RTAs and BITs coevolve by applying a stochastic actor-oriented model of multiplex network evolution. In particular, we examine the roles of (i) cross-network dyadic interinfluences and (ii) within- and cross-network preferential attachments to discuss the dynamic relationships between RTAs and BITs. The results are as follows. First, our estimation supports cross-network dyadic interinfluences. Countries that sign a BIT are willing to establish an RTA, while those that sign an RTA are reluctant to establish a BIT. Second, concerning preferential attachments, countries prefer to sign BITs with partners that have more RTA and BIT links. However, countries tend to form RTAs with partners that have more BIT links but are reluctant to form RTAs with those that have more RTA links. We discuss possible justifications for these results, including arguments regarding the benefits and costs associated with the formation of RTAs and BITs.