Initial Positioning Method for Online and Real-Time Dynamic Graph Drawing of Time Varying Data

Hayashi, Aki; Matsubayashi, Tatsushi; Hoshide, Takahide; Uchiyama, Tadasu

doi:10.1109/iv.2013.57

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…[GdBG12], however, combining it with a visual analysis of the sample data because not all important features are covered by the employed set of metrics. Other approaches as well measure runtime performance [FWSL12], preservation of the mental map [CTB13, FWSL12, HMHU13] or cluster quality [LSCL10]. While evaluation constitutes only a part of the above papers, Brandes and Mader [BM12] focus only on an algorithmic evaluation of offline node‐link approaches: they contrast layout stability to individual layout quality and conclude that only linking nodes of adjacent time steps provides better results than positioning nodes at or near fixed positions.…”

Section: Discussionmentioning

confidence: 99%

“…Also addressing efficiency, Hayashi et al . [HMHU13] investigate the effects of initial node placement on the responsiveness of a layout algorithm. Grabowicz et al .…”

Section: Taxonomy and Classification Of Dynamic Graph Visualization Tmentioning

confidence: 99%

“…By introducing an efficient force-directed online layout algorithm and implementing it on the GPU, Frishman and Tal [FT08b] present a particularly fast layout approach. Also addressing efficiency, Hayashi et al [HMHU13] investigate the effects of initial node placement on the responsiveness of a layout algorithm. Grabowicz et al [GAM14] present an approach to filter streams of changes in large graphs to a manageable size.…”

Section: Ia1 Online Problemmentioning

confidence: 99%

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A Taxonomy and Survey of Dynamic Graph Visualization

Beck

Burch

Diehl

et al. 2016

Computer Graphics Forum

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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Section: Discussionmentioning

confidence: 99%

“…Also addressing efficiency, Hayashi et al . [HMHU13] investigate the effects of initial node placement on the responsiveness of a layout algorithm. Grabowicz et al .…”

Section: Taxonomy and Classification Of Dynamic Graph Visualization Tmentioning

confidence: 99%

Section: Ia1 Online Problemmentioning

confidence: 99%

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A Taxonomy and Survey of Dynamic Graph Visualization

Beck

Burch

Diehl

et al. 2016

Computer Graphics Forum

283

249

View full text Add to dashboard Cite

show abstract

“…When the mapping of the graph takes the form of node link diagrams, the result is a reasonably intuitive dynamic graph visualization. Node-link diagrams remain the most popular approach [7,18,27,30,33,36], with one work combining matrices and animation [51].…”

Section: Background and Related Work 21 Dynamic Visualizationmentioning

confidence: 99%

Dynamic Network Plaid

Lee

Archambault

Nacenta

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram. CCS CONCEPTS • Human-centered computing → Visualization toolkits; User interface programming; Touch screens;

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“…Animation-based techniques simulate the evolution of network by redrawing the network at each time step. Most research in this category focus on solving layout problems to reduce cognitive difficulty and improve efficiency [6,9,22,23,25,36]. Foresight layout was introduced and extended by [9,18,36] to reduce graph change and preserve the mental map.…”

Section: Dynamic Network Analysis and Visualizationmentioning

confidence: 99%