Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs

Abstract

Fig. 1. Responsive matrix cells are a focus+context approach that provides details for a multivariate graph via embedded visualizations in a matrix representation and allows analysts to go from the overview in the matrix to details as well as editing within the cells. c b Abstract-Matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session.

The Pattern is in the Details: An Evaluation of Interaction Techniques for Locating, Searching, and Contextualizing Details in Multivariate Matrix Visualizations

“…It folds the intervening space to guarantee the visibility of multiple focus regions. Responsive matrix cells [38] combine focus+context with semantic zooming to allow analysts to go from the overview of the matrix to details in cells. Given the diversity of focus+context techniques, we tested the effectiveness of four representative lenses derived from Carpendale et al's taxonomy [19]: Cartesian lens [73], two TableLens variations [68], and an adapted fisheye lens [70].…”

“…It first reorders [75] rows and columns to group related elements and then aggregates neighboring cells dependent on the current zoom level. Horak et al [38] provide both geometric and semantic zooming in matrices. However, their technique has not been empirically evaluated.…”

“…For example, analysts frequently use them to explore temporal data [6,7,10,12,30,83,89], such as ecologists studied multi-year international food trade through MMV [43], and biologists studied dynamic Bayesian networks with MMVs to model probabilistic dependencies in gene regulation and brain connectivity across time [81]. Additionally, MMVs can also be used to show multiple attributes of a data point [38,63,72,88] or aggregated values from details [24,26,49,51]. For instance, MMV can help pathologists interpret multiclass classifications by visualizing multiple class probabilities at once [63] in histopathology [85], and MMV can be used to help analyze complex multi-variate geographic data [33].…”

“…The distortion from many focus+context techniques, like a pixel-based fisheye lens, produce irregular shapes of cells that may prohibit effective exploration in MMV. On the other hand, Responsive matrix cells [38], Mélange [27,28], LiveRAC [55], and TableLens [68] are representative focus+context techniques that are applicable to MMV. Overview+detail technique provides two spatially-separate views with different levels of detail.…”

“…Given the diversity of focus+context techniques [27,28,38,55,68], the many ways their distortions could impact the perception and task performance, and their overall good performance in some applications [9,35,74], we compared different lenses in our first study. To identify representative lenses, we followed Carpendale's taxonomy for distortion [19] and identified four lenses: Cartesian lens [73] applies non-linear orthogonal distortion; TableLens [68] with orthogonal distortion (step and stretch); and a fisheye lens technique that is adapted to matrix visualizations [19,70].…”