Integrating Prior Knowledge in Mixed Initiative Social Network Clustering
This addresses the challenge for social scientists in creating meaningful clusters in social networks by providing a more interpretable and guided approach, though it appears incremental as it builds on existing clustering ensemble methods.
The authors tackled the problem of social scientists struggling to use existing clustering algorithms for social networks by introducing PK-clustering, which integrates prior knowledge through a visual analytics interface, resulting in a method that helps users iteratively build knowledge and avoid reliance on black-box algorithms, as demonstrated with early feedback from social scientists.
We propose a new approach -- 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 approach 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, 4) evaluates the consensus between user-selected algorithms, and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach 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.