Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs
Tom Horak, Philip Berger, Heidrun Schumann, Raimund Dachselt, Christian Tominski
Abstract
to Details to Editing 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 997ef684-3adc-4b09-b6af-ce4976a7c072Cited by top-tier papers6
- Input Visualization: Collecting and Modifying Data with Visual RepresentationsNathalie Bressa, Jordan Louis, Wesley Willett, Samuel HuronCHI 2024 · 25 citations
- The Pattern is in the Details: An Evaluation of Interaction Techniques for Locating, Searching, and Contextualizing Details in Multivariate Matrix VisualizationsYalong Yang, Wenyu Xia, Fritz Lekschas, Carolina Nobre et al.CHI 2022 · 15 citations
- 2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual RealityStefan P. Feyer, Bruno Pinaud, Stephen G. Kobourov, Nicolas Brich et al.IEEE VIS 2023 · 12 citations
- BEMTrace: Visualization-Driven Approach for Deriving Building Energy Models from BIMAndreas Walch, Attila Szabó, Harald Steinlechner, Thomas Ortner et al.IEEE VIS 2024 · 5 citations
- BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered ValidationEden Wu, Dishita G. Turakhia, Guande Wu, Christos Koutras et al.IEEE VIS 2025 · 3 citations
Builds on2
- Techniques for Flexible Responsive Visualization DesignJane Hoffswell, Wilmot Li, Zhicheng LiuCHI 2020 · 64 citations
- Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing ApproachCarolina Nobre, Dylan Wootton, Lane Harrison, Alexander LexCHI 2020 · 37 citations
Related papers
- Visual Analytics for Temporal Hypergraph Model ExplorationMaximilian T. Fischer, Devanshu Arya, Dirk Streeb, Daniel Seebacher et al.IEEE VIS 2020 · 33 citations
- HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular DataGuozheng Li, Runfei Li, Zicheng Wang, Chi Harold Liu et al.IEEE VIS 2022 · 18 citations
- CrossSet: Unveiling the Complex Interplay of Two Set-typed Dimensions in Multivariate DataKresimir Matkovic, Rainer Splechtna, Denis Gracanin, Helwig HauserIEEE VIS 2025
- SightBi: Exploring Cross-View Data Relationships with BiclustersMaoyuan Sun, Abdul Rahman Shaikh, Hamed Alhoori, Jian ZhaoIEEE VIS 2021 · 13 citations
- Githru: Visual Analytics for Understanding Software Development History Through Git Metadata AnalysisYoungtaek Kim, Jaeyoung Kim, Hyeon Jeon, Young-Ho Kim et al.IEEE VIS 2020 · 36 citations
