Integrating Prior Knowledge in Mixed-Initiative Social Network Clustering

• Published in: IEEE transactions on visualization and computer graphics Vol. 27; no. 2; pp. 1775 - 1785
• Main Authors: Pister, Alexis, Buono, Paolo, Fekete, Jean-Daniel, Plaisant, Catherine, Valdivia, Paola
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; Computer Science; Heuristic algorithms; Human-Computer Interaction; Humanities and Social Sciences; Inference algorithms; Knowledge; Knowledge acquisition; Machine Learning; mixed-initiative; network visualization; prior knowledge; Scientists; Social network analysis; Social networking (online); Social networks; user interface; Visualization; network visualization; clustering; Social network analysis; prior knowledge; user interface; mixed-initiative
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2020.3030347

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.