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

Tom Horak,Christian Tominski,Heidrun Schumann,Philip Berger,Raimund Dachselt

doi:10.1109/tvcg.2020.3030371

Tom Horak, Christian Tominski + Show 3 more

Open Access

https://doi.org/10.1109/tvcg.2020.3030371

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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.

Highlights

Multivariate graphs consist of nodes, edges, and multivariate data attributes
Typical tasks on multivariate graphs include gaining an overview of the graph structure, assessing the overall similarity of nodes, studying the distribution of attribute values, comparing nodes in detail, and finding relations between attributes and the graph structure [60]
We propose responsive matrix cells (RMCs) as a flexible focus+context approach to embed responsive visualizations into a matrix, either into individual cells or across cohesive submatrices

Summary

Introduction

Multivariate graphs consist of nodes, edges, and multivariate data attributes. An example would be a power grid, where power plants (the nodes) are characterized by quantitative attributes such as maximum capacity or current load. Typical tasks on multivariate graphs include gaining an overview of the graph structure (what is connected to what?), assessing the overall similarity of nodes (which power plants are alike?), studying the distribution of attribute values (what are the characteristics of plants in a sub-grid?), comparing nodes in detail (which plant produces less carbon dioxide?), and finding relations between attributes and the graph structure (are similar plants interconnected?) [60] In addition to these analysis-oriented objectives, it is becoming increasingly important to be able to edit or wrangle data [7, 37]. Data editing can be necessary to correct erroneous data values (implausible power line throughput), and to carry out what-if analyses [69] to test how data characteristics change when certain values are present in the data (would there be sufficient energy when reducing the capacity of some power plants?)

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