Matrix Reordering Methods for Table and Network Visualization

Behrisch, Michael; Bach, Benjamin; Riche, Nathalie Henry; Schreck, Tobias; Fekete, Jean-Daniel

doi:10.1111/cgf.12935

Cited by 168 publications

(190 citation statements)

References 85 publications

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“…ROIs in adjacency matrices can represent topological cliques and clusters, subgraphs, or specific graph motifs resulting in specific visual patterns in the matrix [7]. Adapting snippet exploration to networks requires an appropriate matrix ordering [6] to create visual patterns as well as pattern extraction methods specific to network. These can be topological cluster and motif detection algorithms or visual pattern recognition methods.…”

Section: Discussionmentioning

confidence: 99%

“…Matrices are a common representation for visualizing networks or graphs [6, 43]. Thus, we briefly overview related visualization techniques that focus on large matrices.…”

Section: Related Workmentioning

confidence: 99%

“…Network motifs have frequently been used as first-class objects in network exploration. Visualized in an adjacency matrix, they result in recurring visual patterns [6]. For example, clusters occur as rectangles (similar to domains in interaction matrices ) and highly connected nodes appear as horizontal and vertical lines (similar to flames ).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Lekschas

Bach

Kerpedjiev

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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This paper presents an interactive visualization interface—HiPiler—for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like “snippets”. Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

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Section: Discussionmentioning

confidence: 99%

“…Matrices are a common representation for visualizing networks or graphs [6, 43]. Thus, we briefly overview related visualization techniques that focus on large matrices.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Lekschas

Bach

Kerpedjiev

et al. 2018

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…The features that can be discovered in matrices are strongly influenced by the matrix ordering [BBHR*16]. Consequently, Graffinity supports dynamic re-ordering either based on node attributes, or using matrix reordering algorithms [Fek15].…”

Section: Graffinitymentioning

confidence: 99%

Graffinity: Visualizing Connectivity in Large Graphs

Kerzner

Lex

Sigulinsky

et al. 2017

Computer Graphics Forum

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Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.

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“…Sometimes local variation is introduced, and the distribution is pre-specified [117,80,9]. Such specifications may even be the result of users sketching adjacency matrices [124].…”

Section: Network and Graph Visualizationmentioning

confidence: 99%

Generative Data Models for Validation and Evaluation of Visualization Techniques

Schulz

Nocaj

El‐Assady

et al. 2016

Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization

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We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and unconfounded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.

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Matrix Reordering Methods for Table and Network Visualization

Cited by 168 publications

References 85 publications

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Graffinity: Visualizing Connectivity in Large Graphs

Generative Data Models for Validation and Evaluation of Visualization Techniques

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