Deterministic blockmodelling of signed and two‐mode networks: A tutorial with software and psychological examples

Abstract

Deterministic blockmodelling is a well‐established clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering two‐mode networks). Even though some of the historical foundations for certain types of blockmodelling stem from the psychological literature, applications of deterministic blockmodelling in psychological research are relatively rare. This scarcity is potentially attributable to three factors: a general unfamiliarity with relevant blockmodelling methods and applications; a lack of awareness of the value of partitioning network data for understanding group structures and processes; and the unavailability of such methods on software platforms familiar to most psychological researchers. To tackle the first two items, we provide a tutorial presenting a general framework for blockmodelling and describe two of the most important types of deterministic blockmodelling applications relevant to psychological research: structural balance partitioning and two‐mode partitioning based on structural equivalence. To address the third problem, we developed a suite of software programs that are available as both Fortran executable files and compiled Fortran dynamic‐link libraries that can be implemented in the R software system. We demonstrate these software programs using networks from the literature.

netroles: A Java library for role equivalence analysis in networks

“…Software like graph-tool (Peixoto, 2014), sbm (Chiquet et al, 2023), blockmodels (Leger et al, 2021), StochBlock (Škulj & Žiberna, 2022;Žiberna & Telarico, 2023), and dynsbm (Matias & Miele, 2020) offer stochastic blockmodeling methods derived from stochastic versions of structural equivalence. Packages such as blockmodeling (Žiberna, 2007;Žiberna & Cugmas, 2023), dBlockmodeling (Brusco et al, 2021;Brusco, 2023), Pajek (Batagelj & Mrvar, 2004), and signnet (Schoch, 2023) implement optimizational blockmodeling minimizing some criterion function based on one or more role equivalence concepts.…”

Natural Language Use as Bipartite Networks in Psychology

“…Here, clustering techniques are fundamental to explore the structure of customers' experiences from a particular sample (Teichert et al, 2020) or test if such a structure preserves regardless of the sample it belonged to. The theoretical orientation of these efforts is recently recognized by scholars working on blockmodeling techinques (Brusco et al, 2019) with foundations going back to the structural balance theory (Cartwright & Harary, 1956). Given the variety of clustering techniques to analyze textual data represented as BN, it is impossible to cover them all properly.…”

“…Above and beyond these reasons, an illustration of BNs and how to apply them for the analysis of psycho-linguistic phenomena deserves its own exposition. BNs have strong potential to enrich psychologists' research toolbox and provide a convenient framework for building and testing psychological theories with techniques and concepts developed in the so-called complex systems sciences that are not well-known by psychologists either (Brusco et al, 2019;Correa, 2020). In what follows, the next section defines BNs, summarizes some examples, and provides a generalized pipeline that allows the analysis of natural language.…”