Integrating Prior Knowledge in Mixed Initiative Social Network Clustering

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Direct link to video on YouTube: https://youtu.be/WrRD893EL9U

Keywords

Social network analysis, network visualization, clustering, mixed-initiative, prior knowledge, user interface

Abstract

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 tochoose algorithms, or to evaluate results taking into account theprior knowledgeof the scientists. Our work introduces a new clusteringapproach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledgeof the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly toclustering ensemblemethods),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 acquiredknowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback fromsocial scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoidingbeing overly influenced by the results of often randomly selected black-box clustering algorithms.