Visual Encodings for Networks with Multiple Edge Types

Vogogias, Athanasios; Archambault, Daniel; Bach, Benjamin; Kennedy, Jessie

doi:10.1145/3399715.3399827

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…In this section, we discuss insights gained from understanding what is being evaluated across multiple evaluation studies. In the current literature on DMVNs, evaluation studies are mainly focused on node‐link diagrams [AP12,FM16,LAN20], while very few studied matrix‐based visualizations [VABK20] and compared both techniques [FAM21]. List views are not yet evaluated for their effectiveness in visual exploration and analysis of DMVNs.…”

Section: Discussionmentioning

confidence: 99%

The State of the Art in Visualizing Dynamic Multivariate Networks

Kale

Sun

Papka

2023

Computer Graphics Forum

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Most real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

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Section: Discussionmentioning

confidence: 99%

The State of the Art in Visualizing Dynamic Multivariate Networks

Kale

Sun

Papka

2023

Computer Graphics Forum

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“…adjacency matrices) present significant potential for multi‐variate network visualization. Similarly, a limited number of techniques investigate the visualization of edge attributes: tabular and integrated approaches could be potential areas where this challenge can be tackled [ SSE16 , VABK20 ]. The evaluation analysis shows that very few studies rigorously investigated the trade‐offs and benefits of different multi‐variate visualization techniques – task taxonomies have the potential and purpose to drive more formal evaluations.…”

Section: Surveysmentioning

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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“…Büschel et al investigated the influence of the edge encoding in AR on task performance, concluding that in general different styles can be used [14]. Vogogias et al [62] investigated designs for encoding multiple types of edges in matrices and found task-dependent performance differences. Soni et al and Kypridemou et al investigated the influence of different layout methods on the perception of graph properties in 2D [43,59].…”

Section: Network Comparison and Visualization Metaphorsmentioning

confidence: 99%