About

I’m a computational sociologist, Assistant Professor, and a seasoned R developer and trainer. My main research interests focus on (dynamics of) social networks and mathematical/computational social science as tools for understanding conflict and cooperation. I am an Assistant professor at the Chair of Quantitative Methods and Information Technology, Kozminski University where, among other things, I teach econometrics, social network analysis and some other data-analysis-related courses. I’m also a researcher at the COALESCE Lab, Universitat Autònoma de Barcelona participating in the Patchwork project.

Lately…

I’m busy expressing professional love and passion towards:

  • Statistical modeling of networks with ERGMs based on “partial” network data such as ego-centric samples or aggregate network data (e.g. collected for the Network Scale-Up Method). These will be extremely handy in addressing research questions posed in the PATCHWORK project.
  • Complex network datasets. In particular, assembling them from non-obvious sources in a technically-advanced way. Some say it is Digital Humanities. Examples are projects: Mapping Atlantic Portuguese Empire (MAPE) or Law in social networks of late antique Aphrodito.
  • The Statnet Suite. I contribute code, documentation, and training. Statnet is a suite of R packages for statistical social network analysis, visualization, and modeling focusing on Exponential-family Random Graph Models.
  • My own R packages, which you can find among the projects.

A mini-CV

COALESCE Lab ∙ Universitat Autònoma de Barcelona ∙ 2022 - present

Chair of Quantitative Methods ∙ Kozminski University ∙ 2018 - present

ICM ∙ University of Warsaw ∙ 2010 - 2018

PhD in Sociology ∙ ICS / Utrecht University ∙ 2012

Institute of Philosophy and Sociology ∙ Polish Academy of Sciences ∙ 2003 - 2005

MA in Sociology ∙ University of Warsaw ∙ 2003

ICPSR Summer Program ∙ University of Michigan ∙ 2002

Projects

Patchwork

A Network Science Approach to Social Cohesion in European Societies. An ERC-funded project lead by Miranda Lubbers (UAB).

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Talks that Last

Automated Name Selection for the Network Scale-up Method

The distribution of the number of acquaintances among members of a society is a relevant feature of its social structure. Furthermore, the number of acquaintances (or “degree”) is used for estimating other societal features, such as the size of hard-to-count subpopulations or social cohesion. To estimate the degree, the Network Scale-Up Method (NSUM) asks survey respondents about the number of people they know with a set of first names for which name statistics are available. For this method to be precise, a set of names needs to be selected for the survey that jointly represent the population on a smaller scale in terms of relevant traits such as gender or age. Finding the optimal set of names is a combinatorial problem for which this paper provides a solution approach. The approach can serve other NSUM users, and can be applied to any population for which name statistics distributed over different categories are available. We empirically show that our approach successfully provides subsets of names replicating the population distribution for six countries with very different name statistics.

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Michał Bojanowski

Social scientist

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