Multilevel embeddedness: The case of the global fisheries governance complex

Hollway, James; Koskinen, Johan

doi:10.1016/j.socnet.2015.03.001

Cited by 58 publications

(33 citation statements)

References 50 publications

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“…There is a growing body of research articles and books concerned with multilevel network data, models, and methods (e.g., Wang et al, 2013;Zappa and Lomi, 2015;Lomi et al, 2016;Slaughter and Koehly, 2016;Hollway and Koskinen, 2016;Lazega and Snijders, 2016;Hollway et al, 2017). For the type of multilevel network considered here, existing approaches include…”

Section: Comparison With Existing Approachesmentioning

confidence: 99%

Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms

et al. 2019

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Multilevel network data provide two important benefits for ERG modeling. First, they facilitate estimation of the decay parameters in geometrically weighted terms for degree and triad distributions. Estimating decay parameters from a single network is challenging, so in practice they are typically fixed rather than estimated. Multilevel network data overcome that challenge by leveraging replication. Second, such data make it possible to assess out-of-sample performance using traditional cross-validation techniques. We demonstrate these benefits by using a multilevel network sample of classroom networks from Poland. We show that estimating the decay parameters improves in-sample performance of the model and that the out-of-sample performance of our best model is strong, suggesting that our findings can be generalized to the population of interest.

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Section: Comparison With Existing Approachesmentioning

confidence: 99%

Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms

et al. 2019

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“…This conceptualization that includes all relevant model information in one process state simplifies working with increasingly complex network data, as practiced more and more in recent years and exemplified by recent studies that simultaneously analyze multiple networks (e.g., Boda and Néray 2015;Pál et al 2016;Vörös and Snijders 2017) and, in particular, multimode or multilevel networks Wang et al 2013;Lomi and Stadtfeld 2014;Hollway and Koskinen 2016). …”

Section: Working With Multivariate Process Statesmentioning

confidence: 99%

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

Stadtfeld¹,

Block²

2017

SocScience

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Ample theoretical work on social networks is explicitly or implicitly concerned with the role of interpersonal interaction. However, empirical studies to date mostly focus on the analysis of stable relations. This article introduces Dynamic Network Actor Models (DyNAMs) for the study of directed, interpersonal interaction through time. The presented model addresses three important aspects of interpersonal interaction. First, interactions unfold in a larger social context and depend on complex structures in social systems. Second, interactions emanate from individuals and are based on personal preferences, restricted by the available interaction opportunities. Third, sequences of interactions develop dynamically, and the timing of interactions relative to one another contains useful information. We refer to these aspects as the network nature, the actor-oriented nature, and the dynamic nature of social interaction. A case study compares the DyNAM framework to the relational event model, a widely used statistical method for the study of social interaction data.

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“…In some applications, the block structure is known. An example is multilevel networks, which are popular in network science [e.g., 65,72,41,60,26,27]: e.g., the blocks may correspond to school classes in schools, units of armed forces, and departments of universities. While the block structure is known in some applications, it is unknown in others.…”

Section: Recovery Of Unknown Block Structurementioning

confidence: 99%

Consistent structure estimation of exponential-family random graph models with block structure

Schweinberger¹

2020

Bernoulli

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We consider the challenging problem of statistical inference for exponential-family random graph models based on a single observation of a random graph with complex dependence. To facilitate statistical inference, we consider random graphs with additional structure in the form of block structure. We have shown elsewhere that when the block structure is known, it facilitates consistency results for M -estimators of canonical and curved exponential-family random graph models with complex dependence, such as transitivity. In practice, the block structure is known in some applications (e.g., multilevel networks), but is unknown in others. When the block structure is unknown, the first and foremost question is whether it can be recovered with high probability based on a single observation of a random graph with complex dependence. The main consistency results of the paper show that it is possible to do so under weak dependence and smoothness conditions. These results confirm that exponential-family random graph models with block structure constitute a promising direction of statistical network analysis.

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Multilevel embeddedness: The case of the global fisheries governance complex

Cited by 58 publications

References 50 publications

Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms

Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

Consistent structure estimation of exponential-family random graph models with block structure

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