TL;DR
This paper reviews different methods for blockmodeling multilevel network data, which involve networks measured at multiple levels such as organizations and individuals, and discusses their respective advantages and disadvantages.
Contribution
It introduces and compares several approaches for blockmodeling multilevel networks, including separate analysis, data transformation, and a comprehensive multilevel modeling framework.
Findings
Multiple approaches have distinct advantages and disadvantages.
Transforming networks to a single level can simplify analysis.
Multilevel modeling captures complex inter-level ties effectively.
Abstract
The article presents several approaches to the blockmodeling of multilevel network data. Multilevel network data consist of networks that are measured on at least two levels (e.g. between organizations and people) and information on ties between those levels (e.g. information on which people are members of which organizations). Several approaches will be considered: a separate analysis of the levels; transforming all networks to one level and blockmodeling on this level using information from all levels; and a truly multilevel approach where all levels and ties among them are modeled at the same time. Advantages and disadvantages of these approaches will be discussed.
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