Matching Application Requirements with Dynamic Graph Visualization Profiles

Beck, Fabian; Burch, Michael; Diehl, Stephan

doi:10.1109/iv.2013.2

Cited by 17 publications

(12 citation statements)

References 19 publications

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“…Chen [102] devised a generic approach for detecting and visualizing emerging trends and transient patterns in scientific literature. Other recent work of interest is presented by Beck et al [103,104] and the visualization of evolving graphs with multiple visual metaphors of Burch [105]. The combination of dynamic network visualization with graph sampling techniques is often used [106].…”

Section: Challenges Of Evolving Network' Visualizationmentioning

confidence: 99%

Evolving Networks and Social Network Analysis Methods and Techniques

Cordeiro¹,

Sarmento²,

Brazdil³

et al. 2018

Social Media and Journalism - Trends, Connections, Implications

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Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in networks during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research importance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks.

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Section: Challenges Of Evolving Network' Visualizationmentioning

confidence: 99%

Evolving Networks and Social Network Analysis Methods and Techniques

Cordeiro¹,

Sarmento²,

Brazdil³

et al. 2018

Social Media and Journalism - Trends, Connections, Implications

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show abstract

“…While many of those criteria were discussed and tested for static graphs [BRSG07], Beck et al . [BBD09, BBD13] extend these criteria to dynamic graph visualization and suggest three dimensions grouping the criteria: general aesthetic criteria , dynamic aesthetic criteria and aesthetic scalability criteria . These criteria dimensions can be used for evaluating qualities of dynamic graph visualizations and find the right visualization technique for a given set of tasks and datasets [BBD13].…”

Section: Discussionmentioning

confidence: 99%

“…These characteristics, however, only describe properties of static graphs. They are applicable to each graph individually in the sequence of graphs, but there exist additional dynamic properties [BBD13]. A particularly important one is dynamic variance , which quantifies how much the graph structure changes from one time step to the next.…”

Section: Dynamic Graph Datamentioning

confidence: 99%

“…While this would be one of the most important criteria to discern static graph visualizations, the time dimension adds another central aspect to the visualization when considering dynamic graphs. As Beck et al [BBD09,BBD13] already discussed in this context, the time dimension can be mapped in an animation to a simulated time (time-to-time mapping) or to a space dimension of the generated visualization representing a timeline (time-to-space mapping). Other mappings would be possible-for instance, a mapping of time to colour-but are rarely applied as an independent visualization approach.…”

Section: Taxonomy and Classification Of Dynamic Graph Visualization Tmentioning

confidence: 99%

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A Taxonomy and Survey of Dynamic Graph Visualization

Beck

Burch

Diehl

et al. 2016

Computer Graphics Forum

Self Cite

283

249

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Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. A bibliographic analysis provides insights into the organization and developmentof the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.

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“…39,40 Animation of a series of dynamic graphs has the advantage that the entire screen can be devoted to the drawing of the graph. 41 GraphAEL 42 supports the notion of time slice to visualize evolving graphs with a temporal component.…”

Section: Dynamic Data Visualizationmentioning

confidence: 99%

SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

Ma¹,

Forbes²,

Llano³

et al. 2016

jist

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Study of the behavior of individual members in communities of dynamic networks can help neuroscientists to understand how interactions between neurons in brain networks change over time. Visualization of those temporal features is challenging, especially for networks embedded within spatial structures, such as brain networks. In this article, the authors present the design of SwordPlots, an interactive multi-view visualization system to assist neuroscientists in their exploration of dynamic brain networks from multiple perspectives. Their visualization helps neuroscientists to understand how the functional behavior of the brain changes over time, how such behaviors are related to the spatial structure of the brain, and how communities of neurons with similar functionality evolve over time. To evaluate their application, they asked neuroscientists to use SwordPlots to examine four different mouse brain data sets. Based on feedback, their visualization design can provide neuroscientists with the ability to gain new insights into the properties of dynamic brain networks.

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Matching Application Requirements with Dynamic Graph Visualization Profiles

Cited by 17 publications

References 19 publications

Evolving Networks and Social Network Analysis Methods and Techniques

Evolving Networks and Social Network Analysis Methods and Techniques

A Taxonomy and Survey of Dynamic Graph Visualization

SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

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