A Taxonomy and Survey of Dynamic Graph Visualization

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

doi:10.1111/cgf.12791

Cited by 283 publications

(301 citation statements)

References 148 publications

(238 reference statements)

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“…Previous efforts to classify the visual techniques into taxonomic categories are presented in the literature [2,13,6]. Nevertheless, the area still lacks systems that cover more temporal techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, little attention has been given to some categories as, for example, matrix related techniques. In a recent survey, the visualization of dynamic networks are categorized in animation or timeline [6]. The animation taxonomic category contains the node-link layout, which uses an animation to show the graph drawing, and is sub-divided in General-Purpose Layout and SpecialPurpose Layout.…”

Section: Related Workmentioning

confidence: 99%

“…Users are able to actively interact with these layouts, by selecting nodes or links, zooming in or out, scrolling in the x-y dimensions, and so on. The following sections explain the techniques used in this work together with their respective classifications according to the categories presented in [6] . …”

Section: Visual Layoutsmentioning

confidence: 99%

“…The mapping of a single attribute to multiple visual properties is recommended to highlight the characteristics to be visualized, known as redundant coding [21]. This approach is classified as SpecialPurpose Layout, because we employ the color, shape, size and positioning of nodes and edges to distinguish group of nodes and make the visualization scalable [6].…”

Section: Structural Layoutmentioning

confidence: 99%

See 3 more Smart Citations

DyNetVis

Linhares

Travençolo

Paiva

et al. 2017

Proceedings of the Symposium on Applied Computing

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The concept of networks has been important in the study of complex systems. In networks, links connect pairs of nodes forming complex structures. Studies have shown that networks not only contain structure but may also evolve in time. The addition of the temporal dimension adds complexity on the analysis and requests the development of innovative methods for the visualization of real-life networks. In this paper we introduce the Dynamic Network Visualization System (DyNetVis), a software tool for visualization of dynamic networks. The system provides several tools for user interaction and offers two coordinated visual layouts, named structural and temporal. Structural refers to standard network drawing techniques, in which a single snapshot of nodes and links are placed in a plane, whereas the temporal layout allows for simultaneously visualization of several temporal snapshots of the dynamic network. In addition, we also investigate two approaches for temporal layout visualization: (i) Recurrent Neighbors, a node ordering strategy that highlights frequent connections in time, and (ii) Temporal Activity Map (TAM), a layout technique with focus on nodes activity. We illustrate the applicability of the layouts and interaction functionalities provided by the system in two visual analysis case studies, demonstrating their advantages to improve the overall user experience on visualization and exploratory data analysis on dynamic networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Visual Layoutsmentioning

confidence: 99%

Section: Structural Layoutmentioning

confidence: 99%

See 2 more Smart Citations

DyNetVis

Linhares

Travençolo

Paiva

et al. 2017

Proceedings of the Symposium on Applied Computing

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“…For first-order networks, quite a few works have been presented on visual path analysis and graph comparison. We refer readers to survey papers [3,27,2,28,5] for a comprehensive overview. Bodesinsky et al [8] proposed an interface with coordinated multiple views to explore sequences of events.…”

Section: Geographic Viewmentioning

confidence: 99%

HoNVis: Visualizing and exploring higher-order networks

Tao

Wang

et al. 2017

2017 IEEE Pacific Visualization Symposium (PacificVis)

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Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for the HoN. For applications such as the development of strategies to control species invasion through global shipping which is known to exhibit higher-order dependencies, the existing FoN visualization tools are limited. In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual port levels). Users can quickly identify ports of interest at the global level and specify a port to investigate its higher-order nodes at the individual port level. Investigating a larger-scale impact is enabled through the exploration of HoN at the local level. Using the global ocean shipping network data, we demonstrate the effectiveness of our approach with a real-world use case conducted by domain experts specializing in species invasion. Finally, we discuss the generalizability of this framework to other real-world applications such as information diffusion in social networks and epidemic spreading through air transportation.

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V‐NeuroStack: Open‐source 3D time stack software for identifying patterns in neuronal data

Naik

Kenyon

Taheri

et al. 2022

J of Neuroscience Research

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Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large‐scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V‐NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two‐photon imaging. V‐NeuroStack creates 3D time stacks by stacking 2D time frames for a time‐series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V‐NeuroStack's 3D view is used to explore patterns in dynamic large‐scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual‐line graph provides the ability to explore the raw and first‐derivative values of activity from an individual or a functional cluster of neurons. V‐NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.

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A Taxonomy and Survey of Dynamic Graph Visualization

Cited by 283 publications

References 148 publications

DyNetVis

DyNetVis

HoNVis: Visualizing and exploring higher-order networks

V‐NeuroStack: Open‐source 3D time stack software for identifying patterns in neuronal data

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