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Statistical models for social networks, such as exponential random graphs (ERGMs), have increasingly been used by organizational scholars to study the social interactions inside organizations, departments, and teams. While such models have been successful in providing insights about the local processes that underlie these interactions (such as homophily, reciprocity or transitivity), an additional interesting avenue for research focuses on the role of group-level contextual variables (such as the climate or composition of a team or organization) when considering a multitude of teams or organizations. In this paper we show how, in a team context, integrating team-level factors in a multilevel framework (i.e., a Bayesian hierarchical ERGM) enables us to answer questions about: 1) how team-level contextual factors might provide alternative explanations for the emergence of intra-team ties, and 2) whether any variation in local tendencies between teams might be dependent on such team-level variables (i.e., cross-level interactions). Using data collected among 103 MBA students who were grouped into 18 teams of 5–7 members to work on a project, we study the impact of members' expertise and perceived psychological safety on advice seeking behavior. At a local level, we focus on the effect of these nodal attributes for homophily and differences in advice seeking and giving, while at the team level we incorporate the average expertise and the team's psychological safety climate. Our results show that expert members are more likely to be the recipient of advice ties, but also that this effect is more pronounced in teams where the overall level of expertise is high. For psychological safety, we find that a high psychological safety climate impacts the advice relations among all members in the group, not solely those who themselves perceive a high level of psychological safety, suggesting that the team climate has an impact on all its members. This illustrates how a Bayesian hierarchical ERGM allows us to obtain relevant results even when studying small-sized groups. • This paper discusses how exponential random graph models (ERGMs) can be used to study network relations inside organizations. • We discuss how a Bayesian hierarchical ERGM can be used to simultaneously test local processes and group-level variables. • To illustrate this, we consider the impact of members' expertise and psychological safety on the advice network in 18 teams. • Members are more likely to ask advice from experts and this is more pronounced in teams with a high mean level of expertise. • High psychological safety impacts advice seeking among all team members, not only among members with psychological safety. [ABSTRACT FROM AUTHOR]

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors' social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks. • A Network of Networks (NoN) is a graph whose nodes are graphs on a given node set. • An NoN can be represented as a single multilevel network. • Multilevel network configurations correspond to specific dependencies in the NoN. • We illustrate NoN for dependencies among socially tied actors' semantic networks [ABSTRACT FROM AUTHOR]

COVID-19 has resulted in dramatic and widespread social network interventions across the globe, with public health measures such as distancing and isolation key epidemiological responses to minimize transmission. Because these measures affect social interactions between people, the networked structure of daily lives is changed. Such largescale changes to social structures, present simultaneously across many different societies and touching many different people, give renewed significance to the conceptualization of social network interventions. As social network researchers, we need a framework for understanding and describing network interventions consistent with the COVID-19 experience, one that builds on past work but able to cast interventions across a broad societal framework. In this theoretical paper, we extend the conceptualization of social network interventions in these directions. We follow Valente (2012) with a tripartite categorization of interventions but add a multilevel dimension to capture hierarchical aspects that are a key feature of any society and implicit in any network. This multilevel dimension distinguishes goals, actions, and outcomes at different levels, from individuals to the whole of the society. We illustrate this extended taxonomy with a range of COVID-19 public health measures of different types and at multiple levels, and then show how past network intervention research in other domains can also be framed in this way. We discuss what counts as an effective network, an effective intervention, plausible causality, and careful selection and evaluation, as central to a full theory of network interventions. • We extend the theory of social network interventions to public health measures introduced to manage the COVID-19 pandemic. • Our categorization of interventions adds a multilevel dimension, a key feature of any society and implicit in any network. • We distinguish goals, actions, and outcomes at different levels, from individuals to the whole of the society. • We illustrate our taxonomy by COVID-19 measures and in criminal, organizational, educational, and health contexts. • Intervention outcomes can occur simultaneously across different levels with results that may confound intervention success. • We discuss network effectiveness, causal inferences, the selection and evaluation of interventions, and ethical issues. [ABSTRACT FROM AUTHOR]

• Assessment of implementing two network research studies in conflict-affected DRC. • Negotiating access to field sites and respondents in trust-sensitive contexts. • Mixed methods approaches to social network data collection and analysis. • Importance of trust for collecting quality data in unstable environments. Data collection in social network research has advanced to include online questionnaires, digital metadata mining of internet sites, and the use of remote-sensing technologies. Some scholars however call for more attention to nuanced understandings of ties and contexts in studies of social structure and relationships, evoking practices that characterise the field's foundational works. This article's two studies reference these earlier efforts, drawing on ethnography and primary data collection. Both were undertaken in conflict-affected eastern Democratic Republic of the Congo (DRC) and broadly aimed to refine understandings of public authority and governance. Such research strives to inform humanitarian interventions to support social structures and actors which benefit their communities – however unexpected and unconventional. The first study (2016) employed a novel link-tracing design to examine personal support networks entwining purportedly hostile sub-populations, from combatants to unaffiliated civilians. The second (2018−20) focussed on access to essential social services across different governance arrangements, areas dominated by tenuous alliances of domestic or foreign militias and other actors. Leveraging an egocentric network design, it yielded multilevel relational network chain data. Each study was rife with obstacles related to accessing participants, sampling, reliability, and validity. We reflect on this network collection experience, foregrounding the interdependence between trust and data quality brought into stark relief by the setting's instability and insecurity. This interdependency impacts all social network research, especially when it involves precarious contexts or sensitive topics. [ABSTRACT FROM AUTHOR]

• Assessment of implementing two network research studies in conflict-affected DRC. • Negotiating access to field sites and respondents in trust-sensitive contexts. • Mixed methods approaches to social network data collection and analysis. • Importance of trust for collecting quality data in unstable environments. Data collection in social network research has advanced to include online questionnaires, digital metadata mining of internet sites, and the use of remote-sensing technologies. Some scholars however call for more attention to nuanced understandings of ties and contexts in studies of social structure and relationships, evoking practices that characterise the field's foundational works. This article's two studies reference these earlier efforts, drawing on ethnography and primary data collection. Both were undertaken in conflict-affected eastern Democratic Republic of the Congo (DRC) and broadly aimed to refine understandings of public authority and governance. Such research strives to inform humanitarian interventions to support social structures and actors which benefit their communities – however unexpected and unconventional. The first study (2016) employed a novel link-tracing design to examine personal support networks entwining purportedly hostile sub-populations, from combatants to unaffiliated civilians. The second (2018−20) focussed on access to essential social services across different governance arrangements, areas dominated by tenuous alliances of domestic or foreign militias and other actors. Leveraging an egocentric network design, it yielded multilevel relational network chain data. Each study was rife with obstacles related to accessing participants, sampling, reliability, and validity. We reflect on this network collection experience, foregrounding the interdependence between trust and data quality brought into stark relief by the setting's instability and insecurity. This interdependency impacts all social network research, especially when it involves precarious contexts or sensitive topics. [ABSTRACT FROM AUTHOR]

• We apply ERGM using Bayesian techniques to a large multilevel criminal network. • Collaboration across organised criminal groups depends on illicit market overlap. • In general, market overlap between groups makes collaboration less likely. • Outlaw Motorcycle Gangs are more likely to collaborate when their markets overlap. Collaboration between members of different criminal groups is an important feature of crime that is considered organised, as it allows criminals to access resources and skills in order to exploit illicit economic opportunities. Collaboration across criminal groups is also difficult and risky due to the lack of institutions supporting peaceful cooperation in illicit markets. Thus cross-group collaboration has been thought to take place mostly among small and transient groups. This paper determines whether and under what conditions members of different, larger organised crime groups collaborate with one another. To do so we use intelligence data from the Canadian province of Alberta, centering on criminals and criminal groups engaged in multiple crime types in multiple geographic locations. We apply a multilevel network analytical framework and exponential random graph models using Bayesian techniques to uncover the determinants of cross-group criminal collaboration. We find cross-group collaboration depends not only on co-location, but also on the types of groups to which the criminals are affiliated, and on illicit market overlap between groups. When groups are operating in the same geographically-situated illicit markets their members tend not to collaborate with one another, providing evidence for the difficulty or undesirability of cross-group collaboration in illicit markets. Conversely, members of Outlaw Motorcycle Gangs are more likely to collaborate across groups when markets overlap, suggesting the superior capacity and motivation of biker gangs to coordinate criminal activity. Our paper contributes to the understanding of criminal networks as complex, emergent, and spatially embedded market phenomena. [ABSTRACT FROM AUTHOR]

• Questions of time, order and sequence are critical in analysis of narrative. • We present a dynamic measure of the relative importance of characters in fictional texts. • Illustrated through analysis of gender in 2015 film Star Wars: The Force Awakens. • Narrative text represented as dynamic network based on character interaction. • Measure helps illuminate narrative dynamics which static measures cannot capture. The tools of social network analysis offer a promising framework for studying fictional texts and the relational activity of the characters therein. The goal of this paper is to offer both a conceptual refinement of the project of measuring the centrality of characters within narratives using network tools, as well as the proposal of a novel measure with which to do so. Conceptually, we argue that as questions of time, order and sequence are central in narratives, measures of characters' narrative importance should be based on dynamic network representations which respect the time-ordering of narrative events. We suggest a directed dynamic measure of relative character importance based on character interactions and illustrate it through an examination of gender in the 2015 film Star Wars: The Force Awakens. We find that the measure helps illuminate important narrative dynamics which cannot be captured by static measures, and presents a platform on which future character network research can productively build. [ABSTRACT FROM AUTHOR]

Two mode social network data consisting of actors attending events is a common type of social network data. For these kinds of data it is also common to have additional information about the timing or sequence of the events. We call data of this type two-mode temporal data. We explore the idea that actors attending events gain information from the event in two ways. Firstly the event itself may provide information or training; secondly, as co-attendees interact, they may pass on skills or information they have gleaned from other events. We propose a method of measuring these gains and demonstrate its usefulness using the classic Southern Women Data and a covert network dataset. [ABSTRACT FROM AUTHOR]

While several models for analysing longitudinal network data have been proposed, their main differences, especially regarding the treatment of time, have not been discussed extensively in the literature. However, differences in treatment of time strongly impact the conclusions that can be drawn from data. In this article we compare auto-regressive network models using the example of TERGMs – a temporal extensions of ERGMs – and process-based models using SAOMs as an example. We conclude that the TERGM has, in contrast to the ERGM, no consistent interpretation on tie-level probabilities, as well as no consistent interpretation on processes of network change. Further, parameters in the TERGM are strongly dependent on the interval length between two time-points. Neither limitation is true for process-based network models such as the SAOM. Finally, both compared models perform poorly in out-of-sample prediction compared to trivial predictive models. [ABSTRACT FROM AUTHOR]

The exponential random graph model (ERGM) is a well-established statistical approach to modelling social network data. However, Monte Carlo estimation of ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of full networks that can be fitted. We demonstrate the use of snowball sampling and conditional estimation to estimate ERGM parameters for large networks, with the specific goal of studying the validity of inference about the presence of such effects as network closure and attribute homophily. We estimate parameters for snowball samples from the network in parallel, and combine the estimates with a meta-analysis procedure. We assess the accuracy of this method by applying it to simulated networks with known parameters, and also demonstrate its application to networks that are too large (over 40 000 nodes) to estimate social circuit and other more advanced ERGM specifications directly. We conclude that this approach offers reliable inference for closure and homophily. [ABSTRACT FROM AUTHOR]

This paper explores how bilateral and multilateral clustering are embedded in a multilevel system of interdependent networks. We argue that in complex systems in which bilateral and multilateral relations are themselves interrelated, such as global fisheries governance, embeddedness cannot be reduced to unipartite or bipartite clustering but implicates multilevel closure. We elaborate expectations for ties’ multilevel embeddedness based on network theory and substantive considerations and explore them using a multilevel ERGM. We find states’ bilateral ties are embedded in their shared membership in multilateral fisheries agreements, which is itself clustered around foci represented by similar content and treaty secretariats. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a Bayesian data augmentation scheme for analyzing ERGM with partially observed data. [•] We demonstrate estimation and predictive distributions for three quarters of ties unobserved. [•] We demonstrate analysis when a particularly influential attribute is only partially observed. [Copyright &y& Elsevier]

Abstract: A central part of relational ties between social actors is constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards. [Copyright &y& Elsevier]

Abstract: This paper focuses on how to extend the exponential random graph models to take into account the geographical embeddedness of individuals in modelling social networks. We develop a hierarchical set of nested models for spatially embedded social networks, in which, following , an interaction function between tie probability and Euclidean distance between nodes is introduced. The models are illustrated by an empirical example from a study of the role of social networks in understanding spatial clustering in unemployment in Australia. The analysis suggests that a spatial effect cannot solely explain the emergence of organised network structure and it is necessary to include both spatial and endogenous network effects in the model. [ABSTRACT FROM AUTHOR]