Integrating Prior Knowledge in Mixed Initiative Social Network Clustering


We propose a new paradigm - called PK-clustering - to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering paradigm and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods),3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 5)evaluates the consensus between user-selected algorithms and 6) allows users to review details and iteratively update the acquired knowledge. We believe our clustering paradigm offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often-randomly selected black-box clustering algorithms


Alexis Pister, Paolo Buono, Jean-Daniel Fekete, Catherine Plaisant, Paola Valdivia, Integrating Prior Knowledge in Mixed Initiative Social Network Clustering, IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2021, 27 (2), pp.1775 - 1785. DOI: ⟨10.1109/TVCG.2020.3030347arXiv


Online Demo

You can play with a web prototype of the program by clicking on this link.


Our prototype is implemented using PAOHVis, we support all the formats supported by PAOHVis.

Case Studies Using PK-clustering


This work has been funded by the DATAIA HistorIA project, and by the CHIST ERA IVAN project.


E-mail all co-authors

  • Alexis Pister
  • Paola Valdivia
  • Paolo Buono
  • Catherine Plaisant
  • Jean-Daniel Fekete