Integrating Prior Knowledge in Mixed Initiative Social Network Clustering
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Alexis Pister, Paolo Buono, Jean-Daniel Fekete, Catherine Plaisant, Paola Valdivia
2020
Abstract
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 describe our paradigm using an initial functional
prototype, then provide two examples of use and early feedback from social
scientists. 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.
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