Parallel Edge Splatting for Scalable Dynamic Graph Visualization

Burch, Michael; Vehlow, Corinna; Beck, Fabian; Diehl, Stephan; Weiskopf, Daniel

doi:10.1109/tvcg.2011.226

Cited by 159 publications

(100 citation statements)

References 33 publications

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“…However, 3D can be cluttered, and has occlusion and other perceptual limitations. An interesting 2D approach based on parallel coordinates was proposed by Burch et al [21], where vertices are ordered and positioned on several vertical parallel lines, and directed edges connect these vertices from left to right. The graph of each timestep is thus displayed between two consecutive vertical axes.…”

Section: Static Temporal Plotsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, its the structure of the dynamic graph that evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

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Section: Static Temporal Plotsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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“…In the specific case of graphs, a dynamic network [8,34] can evolve in different ways over time. Most previous approaches for visualizing dynamic graphs can be classified according to our own taxonomy in Figure 2, which shows each node with a changing color representing a numerical attribute.…”

Section: Related Work 21 Visualization Of Dynamic Graphsmentioning

confidence: 99%

DiffAni: Visualizing Dynamic Graphs with a Hybrid of Difference Maps and Animation

Rufiange

McGuffin

2013

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Given a dynamic graph defined over a series of time slices, our DiffAni hybrid visualization displays it as a sequence of consecutive tiles. Each tile may show one or more time slices, and there are three kinds of tiles: diff tiles (indicated with a solid border and two time slice numbers below them, e.g., the left-most tile), animation tiles (indicated with a dashed border and two time slice numbers below them, e.g., the tile covering time slices 2 to 3), and small multiple tiles (indicated with a solid border and a single time slice number below them, e.g., the right-most tile). Nodes and edges are colored in red if they are being removed, or green if they are being added, over time slices. The radial menu above is being used to convert a diff tile into two small multiple ("SM") tiles.Abstract-Visualization of dynamically changing networks (graphs) is a significant challenge for researchers. Previous work has experimentally compared animation, small multiples, and other techniques, and found trade-offs between these. One potential way to avoid such trade-offs is to combine previous techniques in a hybrid visualization. We present two taxonomies of visualizations of dynamic graphs: one of non-hybrid techniques, and one of hybrid techniques. We also describe a prototype, called DiffAni, that allows a graph to be visualized as a sequence of three kinds of tiles: diff tiles that show difference maps over some time interval, animation tiles that show the evolution of the graph over some time interval, and small multiple tiles that show the graph state at an individual time slice. This sequence of tiles is ordered by time and covers all time slices in the data. An experimental evaluation of DiffAni shows that our hybrid approach has advantages over non-hybrid techniques in certain cases.

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“…For large graphs and/or a large number of time moments, a scalability challenge emerges. In the network visualization community, this is often mitigated by suitable node layouts [18]. This is however not applicable to geolocated graphs, as the understanding of geographic distribution is diminished.…”

Section: Related Workmentioning

confidence: 99%

“…Many people go to the center for work in the morning (around 9 o'clock) or move through the center for reaching their destinations. In the evening of working days (hours [18][19], working people move back from the center to the periphery, whereas some people move to the center (see Fig. 5).…”

Section: Mobility In Greater London Areamentioning

confidence: 99%

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Landesberger

Brodkorb

Roskosch

et al. 2016

IEEE Trans. Visual. Comput. Graphics

190

115

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Fig. 1. Hourly flows of residential Twitter users in Greater London area over a week are spatially and temporally simplified to gain an overview of mobility dynamics. A calendar view shows the temporal cluster distribution over a week. Each map displays movements between spatial aggregates in a temporal cluster. Movement direction is represented by a color gradient from dark to light blue.Abstract-Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time intervals) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population.

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Parallel Edge Splatting for Scalable Dynamic Graph Visualization

Cited by 159 publications

References 33 publications

Temporal Multivariate Networks

Temporal Multivariate Networks

DiffAni: Visualizing Dynamic Graphs with a Hybrid of Difference Maps and Animation

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

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