Visualizing Group Structures in Graphs: A Survey

Vehlow, Corinna; Beck, Fabian; Weiskopf, Daniel

doi:10.1111/cgf.12872

Cited by 67 publications

(53 citation statements)

References 182 publications

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“…A less common example is the introduction of a quality metric related to the visual group identification task [VBW17] in order to compare different layout adaptation strategies [NOB15].…”

Section: Relational Datamentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

108

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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“…A less common example is the introduction of a quality metric related to the visual group identification task [VBW17] in order to compare different layout adaptation strategies [NOB15].…”

Section: Relational Datamentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

108

View full text Add to dashboard Cite

show abstract

“…Quite naturally, the visualization of edge group structures, as surveyed by Vehlow et al . [VBW17], presents a lot of similarities with multilayer network visualization and some work cited (e.g. Detangler) can be easily adapted especially for cross layer connectivity and layer reconfiguration (Task categories A and C ).…”

Section: Survey Of Multilayer Graph Visualizationsmentioning

confidence: 99%

The State of the Art in Multilayer Network Visualization

McGee

Ghoniem

Mélançon

et al. 2019

Computer Graphics Forum

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Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

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“…Clustering and filtering groups: To visualize which higherlevel group a given node belongs to, we use a common contour based approach, which has been shown to be versatile in previous research [11]. As visible in fig.…”

Section: Layout and Node Placementmentioning

confidence: 99%

Evo-Clocks: Software Evolution at a Glance

Alexandru

Proksch

Behnamghader

et al. 2019

2019 Working Conference on Software Visualization (VISSOFT)

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Understanding the evolution of a project is crucial in reverse-engineering, auditing and otherwise understanding existing software. Visualizing how software evolves can be challenging, as it typically abstracts a multi-dimensional graph structure where individual components undergo frequent but localized changes. Existing approaches typically consider either only a small number of revisions or they focus on one particular aspect, such as the evolution of code metrics or architecture. Approaches using a static view with a time axis (such as line charts) are limited in their expressiveness regarding structure, and approaches visualizing structure quickly become cluttered with an increasing number of revisions and components. We propose a novel trade-off between displaying global structure over a large time period with reduced accuracy and visualizing fine-grained changes of individual components with absolute accuracy. We demonstrate how our approach displays changes by blending redundant visual features (such as scales or repeating data points) where they are not expressive. We show how using this approach to explore software evolution can reveal ephemeral information when familiarizing oneself with a new project. We provide a working implementation as an extension to our open-source library for fine-grained evolution analysis, LISA.

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Visualizing Group Structures in Graphs: A Survey

Cited by 67 publications

References 182 publications

Quality Metrics for Information Visualization

Quality Metrics for Information Visualization

The State of the Art in Multilayer Network Visualization

Evo-Clocks: Software Evolution at a Glance

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