Representing Micro-Macro Linkages by Actor-based Dynamic Network Models
Authored by Tom A B Snijders, Christian E G Steglich
Date Published: 2015
DOI: 10.1177/0049124113494573
Sponsors:
United States National Institutes of Health (NIH)
Platforms:
R
RSiena
Model Documentation:
Other Narrative
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Mathematical description
Model Code URLs:
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Abstract
Stochastic actor-based models for network dynamics have the primary aim
of statistical inference about processes of network change, but may be
regarded as a kind of agent-based models. Similar to many other
agent-based models, they are based on local rules for actor behavior.
Different from many other agent-based models, by including elements of
generalized linear statistical models they aim to be realistic detailed
representations of network dynamics in empirical data sets. Statistical
parallels to micro-macro considerations can be found in the estimation
of parameters determining local actor behavior from empirical data, and
the assessment of goodness of fit from the correspondence with
network-level descriptives. This article studies several network-level
consequences of dynamic actor-based models applied to represent
cross-sectional network data. Two examples illustrate how network-level
characteristics can be obtained as emergent features implied by
microspecifications of actor-based models.
Tags
Social networks
Sociology
Random graph models