Visual Techniques to Support Exploratory Analysis of Temporal Graph Data

Kerracher, Natalie; Kennedy, Jessie; Chalmers, Kevin; Graham, Martin

doi:10.2312/eurovisshort.20151133

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Within these goals, DynTrix design tasks are as follows. We map our proposed tasks to the taxonomy by Kerracher et al [KKCG15] (indicated in brackets at the end of each task) to ground and corroborate our task analysis and foster future comparisons and experiments with DynTrix.…”

Section: Design Goals Tasks Intended Usersmentioning

confidence: 70%

DynTrix: A Hybrid Representation for Dynamic Graphs

Vago,

Archambault,

Arleo

2024

Computer Graphics Forum

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Hybrid graph representations combine two or more network visualization techniques in a unique drawing, simultaneously leveraging their strong traits. Since their introduction in the early 2000s, hybrid representations have gained significant research interest, with the introduction of new techniques and comparative user studies. However, all this research has not considered dynamic graphs. In this paper, we investigate hybrid graph representations in a dynamic network context and present DynTrix. Our system uses the NodeTrix representation as a basis, but the research extends this representation to the dynamic network domain. DynTrix supports automatic or manually created clusters/matrices across time. Drawing stability is implemented through aggregation and users can rearrange the nodes/matrix positions and pin them. DynTrix visualizes the temporal dynamics of the network through a combination of movement and element highlighting. We also introduce the concept of volatility, that allows the identification of actors in the network that are the most volatile. Matrices can be ordered such that stable cores gravitate towards the centre of the matrix. We integrate this technique in a visual analytics application for the exploration of offline dynamic networks and evaluate our system through case studies and qualitative expert interviews. Experts agree on the capabilities of the system, noting its potential for the analysis of dynamic networks through hybrid representations.

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Section: Design Goals Tasks Intended Usersmentioning

confidence: 70%

DynTrix: A Hybrid Representation for Dynamic Graphs

Vago,

Archambault,

Arleo

2024

Computer Graphics Forum

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“… Open Challenges : For future work the authors propose conducting user studies assessing the most important tasks in the proposed taxonomy, as well as, categorizing visualization techniques according to their task support. The latter has already been investigated in follow‐up work by the authors [ KKCG15 ].…”

Section: Task Taxonomiesmentioning

confidence: 99%

Are We There Yet? A Roadmap of Network Visualization from Surveys to Task Taxonomies

Filipov

Arleo

Miksch

2023

Computer Graphics Forum

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Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever‐growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a “roadmap” detailing the current research trends in the field of network visualization. We design our contribution as a meta‐survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work.

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