Toward a deeper understanding of Visualization through keyword analysis

TL;DR

This work addresses the challenge of diverse and inconsistent vocabulary in visualization research by performing a bottom-up co-word analysis across author keywords, expert-coded keywords, and PCS taxonomy keywords from IEEE VIS venues. It builds topic clusters and strategic diagrams to reveal motor themes and track rising and declining keywords, while releasing KeyVis.org to browse and analyze over 2600 keywords. Key findings show distinct central themes across data sources (e.g., graph visualization and flow visualization as motor themes in author keywords; surfaces and volume rendering in expert keywords; data transformation in PCS) and rising emphasis on interaction, evaluation, and multidimensional data. The study provides a practical pathway toward a common vocabulary and a comprehensive taxonomy, with implications for communication, reviewer assignment, and taxonomy maintenance, while acknowledging limitations and outlining future automation and taxonomy-development opportunities.

Abstract

We present the results of a comprehensive analysis of visualization paper keywords supplied for 4366 papers submitted to five main visualization conferences. We describe main keywords, topic areas, and 10-year historic trends from two datasets: (1) the standardized PCS taxonomy keywords in use for paper submissions for IEEE InfoVis, IEEE Vis-SciVis, IEEE VAST, EuroVis, and IEEE PacificVis since 2009 and (2) the author-chosen keywords for papers published in the IEEE Visualization conference series (now called IEEE VIS) since 2004. Our analysis of research topics in visualization can serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an application that allows visualization researchers to easily browse the 2600+ keywords used for IEEE VIS papers during the past 10 years, aiming at more informed and, hence, more effective keyword selections for future visualization publications.

Figures (6)

• Figure 1: Strategic diagram to characterize the topic clusters (after He:1999:KDT).
• Figure 2: The frequency of the author-assigned keywords (2004--2013, published VIS papers), sorted by their rank (both axes log-transformed).
• Figure 3: Strategic diagrams of the cluster results for \ref{['fig:author-centrality-density']} author-assigned, \ref{['fig:expert-centrality-density']} expert, and \ref{['fig:pcs-centrality-density']} PCS keywords. Black lines indicate the medians.
• Figure 4: Keyword map from hierarchical clustering of author-assigned keywords; showing only connected nodes with correlation strength $\geq$ 0.13. Circle areas correspond to number of occurrences of the keyword in the dataset, link width corresponds to correlation strength, and color distinguished node clusters. We chose a link threshold based on visual inspection of the resulting graph to generate a manageable and readable layout. Isolated nodes were removed from the image.
• Figure 5: Keyword map from hierarchical clustering of the expert-coded keywords; showing only connected nodes with correlation strength $\geq$ 0.11.