Real-time interactive visualization of large networks on a tiled display system

Brinkmann, G.G.; Rietveld, Kristian F. D.; Verbeek, Fons J.; Takes, Frank W.

doi:10.1016/j.displa.2022.102164

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…Brinkmann et al 14 proposed a GPU implementation of the ForceAtlas2 algorithm, which later has been extended for on multiple GPUs in a tiled display environment. 15 Linsenmaier 16 accelerated the Brinkmann et al algorithm implementation 15 to a speedup of three to six times by using an optimized version of the Barnes Hut algorithm. In addition, network algorithms have been ported to the GPU.…”

Section: Related Workmentioning

confidence: 99%

GPU-based, interactive exploration of large spatiotemporal climate networks

Buschmann

Hoffmann

Agarwal

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This paper introduces the Graphics Processing Unit (GPU)-based tool Geo-Temporal eXplorer (GTX), integrating a set of highly interactive techniques for visual analytics of large geo-referenced complex networks from the climate research domain. The visual exploration of these networks faces a multitude of challenges related to the geo-reference and the size of these networks with up to several million edges and the manifold types of such networks. In this paper, solutions for the interactive visual analysis for several distinct types of large complex networks will be discussed, in particular, time-dependent, multi-scale, and multi-layered ensemble networks. Custom-tailored for climate researchers, the GTX tool supports heterogeneous tasks based on interactive, GPU-based solutions for on-the-fly large network data processing, analysis, and visualization. These solutions are illustrated for two use cases: multi-scale climatic process and climate infection risk networks. This tool helps one to reduce the complexity of the highly interrelated climate information and unveils hidden and temporal links in the climate system, not available using standard and linear tools (such as empirical orthogonal function analysis).

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Section: Related Workmentioning

confidence: 99%

GPU-based, interactive exploration of large spatiotemporal climate networks

Buschmann

Hoffmann

Agarwal

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Another recent approach for visualizing large real-world (social) network data on a high-resolution tiled display system was introduced on a tiled display system consisting of multiple screens [15]. The high resolution tiled display approach used GPUs to ensure an interactive setting with real-time visualization.…”

Section: Many Visualization Tools Have Been Introduced In Recent Yearsmentioning

confidence: 99%

Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

Aris¹,

Ramasamy²,

Aris³

et al. 2022

IJACSA

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Through the growth of portals and venues to publish academic publications, the number of academic publications is growing exponentially in recent years. An effective exploration and fast navigation in the collection of academic publications become an urgent need to help academic researchers find publications related to their research and the surrounding community. A scholar network visualization approach is proposed to help users to explore a large number of academic publications concerning the strength of the relationship between each publication. The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. The proposed approach visualizes data by incorporating a force-directed graph with weighted nodes and vertices to give more descriptive information of millions of raw data such as author names, publication title, publication year, publication venue and number of citations from the scholar network dataset. By introducing a weighted relationship in the network visualization, the proposed approach can give a more insightful detail of each publication such as a highly cited publication by looking at and exploring the generated interactive graph. The proposal is targeted to be incorporated into a larger-scale scholar network analytical dashboard that can offer various visualization approaches under one flagship application.

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Real-time interactive visualization of large networks on a tiled display system

Cited by 2 publications

References 36 publications

GPU-based, interactive exploration of large spatiotemporal climate networks

GPU-based, interactive exploration of large spatiotemporal climate networks

Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

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