A Sparse Stress Model

Ortmann, Mark; Klimenta, Mirza; Brandes, Ulrik

doi:10.7155/jgaa.00440

Cited by 21 publications

(27 citation statements)

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“…1138_bus, left: result from[14], right: our result plat1919, left: result from[26], right: our result USpowerGrid, left: result from[14], right: our result wards, only small changes occur. After 47 iterations with 35 accepted SOR values, one of the stop criteria is fulfilled, and the algorithm terminates.…”

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confidence: 82%

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Data-driven graph drawing techniques with applications for conveyor systems

et al. 2020

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The visualization of conveyor systems in the sense of a connected graph is a challenging problem. Starting from communication data provided by the IT system, graph drawing techniques are applied to generate an appealing layout of the conveyor system. From a mathematical point of view, the key idea is to use the concept of stress majorization to minimize a stress function over the positions of the nodes in the graph. Different to the already existing literature, we have to take care of special features inspired by the real-world problems.

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confidence: 82%

“…There are several other methods to make the stress majorization scalable to large graphs, e.g. the sparse stress model [26] or MARS [22]. As already mentioned, Zheng et al [30] proposed another method to minimize the stress function.…”

Section: Stress Majorizationmentioning

confidence: 99%

Data-driven graph drawing techniques with applications for conveyor systems

et al. 2020

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“…Stress is a measurement of drawing quality used in many experiments [15], [38], [46], [57]. It measures how node position reflects the shortest path distance which is a important factor for graph readability as the Euclidean distance is often used to judge how close two nodes are in a graph [29].…”

Section: Methodsmentioning

confidence: 99%

“…Uniformly scaling node positions changes the measure of stress even though the layout is the same [38], [46], [57]. In order to compare methods as fairly as possible, we used a strategy where scale-independent values of stress are compared as follows.…”

Section: Graph Scalingmentioning

confidence: 99%

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Event-Based Dynamic Graph Visualisation

Simonetto

Archambault

Kobourov

2020

IEEE Trans. Visual. Comput. Graphics

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Dynamic graph drawing algorithms take as input a series of timeslices that standard, force-directed algorithms can exploit to compute a layout. However, often dynamic graphs are expressed as a series of events where the nodes and edges have real coordinates along the time dimension that are not confined to discrete timeslices. Current techniques for dynamic graph drawing impose a set of timeslices on this event-based data in order to draw the dynamic graph, but it is unclear how many timeslices should be selected: too many timeslices slows the computation of the layout, while too few timeslices obscures important temporal features, such as causality. To address these limitations, we introduce a novel model for drawing event-based dynamic graphs and the first dynamic graph drawing algorithm, DynNoSlice, that is capable of drawing dynamic graphs in this model. DynNoSlice is an offline, force-directed algorithm that draws event-based, dynamic graphs in the space-time cube (2D+time). We also present a method to extract representative small multiples from the space-time cube. To demonstrate the advantages of our approach, DynNoSlice is compared with state-of-the-art timeslicing methods using a metrics-based experiment. Finally, we present case studies of event-based dynamic data visualised with the new model and algorithm.

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