Bayesian Meta-Analysis of Social Network Data via Conditional Uniform Graph Quantiles

Bibliographic Data

ID6169948
AuthorsC T Butts (0000-0002-7911-9834, University of California, Irvine, corresponding author)
Year2011
Volume41
Issue1
Pages257-298
Publication date2011-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methodology (JOURNAL)
Journal identifiersISSN: 0081-1750 • E-ISSN: 1467-9531
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1111/j.1467-9531.2011.01240.x
OpenAlexW1883104127
LanguageEN
Citations received6
References cited67

Many basic questions in the social network literature center on the distribution of aggregate structural properties within and across populations of networks. Such questions are of increasing relevance given the growing availability of network data suitable for meta-analytic studies, as well as the rise of study designs that involve the collection of data on multiple networks drawn from a larger population. Despite this, little work has been done on model-based inference for the properties of graph populations, or on methods for comparing such populations. Here, we attempt to rectify this gap by introducing a family of techniques that combines an existing approach to the identification of structural biases in network data (the use of conditional uniform graph quantiles) with strategies drawn from nonparametric Bayesian analysis. Conditional uniform graph quantiles are the quantiles of an observed structural property in the reference distribution produced by evaluating that property over all graphs with certain fixed characteristics (e.g., size or density). These quantiles have long been used to measure the extent to which a property of interest on a single network deviates from what would be expected given that network's other characteristics. The methods introduced here employ such quantile information to allow for principled inference regarding the distribution of structural biases within (and comparison across) populations of networks, given data sampled at the network level. The data requirements of these methods are minimal, thus making them well-suited to meta-analytic applications for which complete network data (as opposed to summary statistics) are often unavailable. The structural biases inferred using these methods can be expressed in terms of posterior predictives for familiar and easily communicated quantities, such as p-values. In addition to the methods themselves, we present algorithms for posterior simulation from this model class, illustrating their use with applications to the analysis of social structure within urban communes and radio communications among emergency personnel. We also discuss how this approach may applied to quantiles arising from other reference distributions, such as those obtained using general exponential-family random graph models

Conditional probability distribution · Data mining · Econometrics · Inference · Nonparametric statistics · Quantile · Statistics · Artificial Intelligence · Bayesian Methods and Mixture Models · Complex Network Analysis Techniques · Computer Science · Mathematics · Statistical Methods and Bayesian Inference

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