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Abstract A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes.
Essentialism is an ontological belief that there exists an underlying essence to a category. This article advances and tests in three studies the hypothesis that communication about a social category, and expected or actual mutual validation, promotes essentialism about a social category. In Study 1, people who wrote communications about a social category to their ingroup audiences essentialized it more strongly than those who simply memorized about it. In Study 2, communicators whose messages about a novel social category were more elaborately discussed with a confederate showed a stronger tendency to essentialize it. In Study 3, communicators who elaborately talked about a social category with a naive conversant also essentialized the social category. A meta-analysis of the results supported the hypothesis that communication promotes essentialism. Although essentialism has been discussed primarily in perceptual and cognitive domains, the role of social processes as its antecedent deserves greater attention.
This article presents a conceptual framework for clarifying the network hypotheses embedded in policy theories and how they relate to macrolevel political institutions and microlevel political behavior. We then describe the role of statistical models of networks for testing these hypotheses, including the problem of operationalizing theoretical concepts with the parameters of statistical models. Examples from existing theories of the policy process and empirical research are provided and potential extensions are discussed.
Longitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established in the Australian state of Victoria in September 2020 to collect epidemiological, social, psychological and behavioural data from priority populations. It aims to understand changing public attitudes, behaviours and experiences of COVID-19 and inform epidemic modelling and support responsive government policy.
In this study we examine the structural logic underlying complex intraorganizational networks. Drawing on different propositions about structural regularities in networks and using a comparative case study, we empirically investigate the structural logic of collaborative networks for the strategic decision process in two German corporations. In both organizations, data were gathered on cooperative relationships between all managers belonging to the top two management levels. We model structural regularities at the dyadic and the extradyadic level by applying a class of multivariate exponential random graph models. Our findings contribute to the existing literature in three ways: (1) Although networks are particularly likely to exhibit some types of structural regularities (e.g., reciprocity and transitivity), there are other relational forms such as cycles that seem to be of limited relevance. (2) Structural regularities are not limited to a single type of relation but may comprise instrumental and affective relational ties simultaneously. (3) An organization's formal cooperation structure has surprisingly limited influence on the structural patterns of cooperation, whereas friendship ties are embedded in managers' regular interaction patterns in many different ways.
The development of policy interventions to enhance the sustainability of our future often depends on the capacity to understand and model relevant social systems and to predict the consequences of policy change.To this end, we describe in this paper a suite of statistical models for the structure and dynamics of network-based social systems.In these models, global network structure is hypothesised to arise as the outcome of dynamic, interactive processes occurring within local neighbourhoods of a network.A hierarchy of such processes and model specifications are described.Models can be estimated from a number of possible data structures, including single or panel observations, and partial data structures obtained through certain types of network sampling schemes.Using several illustrative applications, we describe how the models can be used to extend our understanding of social systems and build theoretically plausible and empirically-grounded simulation models.Advantages of the approach include a capacity to quantify uncertainty and its implications, and the facility to assess the extent to which a model captures features of the social system not parameterized in the model.We conclude with a sketch of future work designed to improve our understanding of ways to enhance sustainability within network-based social systems.
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