BISoN: A Bayesian Framework for Inference of Social Networks
BISoN: A Bayesian Framework for Inference of Social Networks
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DOI：
10.1111/2041-210x.14171
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Jordan D. A. Hart;M. Weiss;D. Franks;L. Brent
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文献类型：
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作者：
Jordan D. A. Hart;M. Weiss;D. Franks;L. Brent
社交网络通常是由边缘权重的点估计构建的。在许多情况下，边权是从观测数据中推断出来的，估计的不确定性可能受到各种因素的影响。虽然这在以前的工作中已经得到承认，但明确量化边缘权重不确定性的方法尚未被广泛采用，并且对于许多常见类型的数据仍未开发。此外，现有方法无法处理观测数据中经常发现的一些复杂性，并且不能将边缘权重的不确定性传播到后续的统计分析中。我们介绍了一个统一的贝叶斯框架，用于基于观测数据的社交网络建模。这个框架，我们称之为BISoN，可以容纳许多常见类型的观察社会数据，可以在观察水平上捕获混淆和模型效应，并且与社会网络分析中使用的流行方法完全兼容。我们展示了如何将该框架应用于常见类型的数据，以及如何执行各种类型的下游统计分析，包括对网络属性的非随机关联测试和回归。我们的框架为检验新型假设、充分利用观测数据集和提高科学推论的可靠性提供了机会。我们已经提供了示例R脚本，以支持采用该框架。
Social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses. We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods used in social network analysis. We show how the framework can be applied to common types of data and how various types of downstream statistical analyses can be performed, including non-random association tests and regressions on network properties. Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made example R scripts available to enable adoption of the framework.