Texture-based edge bundling: A web-based approach for interactively visualizing large graphs

Wu, Jieting; Yu, Lina; Yu, Hongfeng

doi:10.1109/bigdata.2015.7364046

Cited by 12 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within NeuroCave, node selection and the edge display are synchronized between the two side-by-side views: selecting a node in one view activates the node and displays its corresponding edges across both viewing areas, independent of the chosen configuration for each side. In order to make real-time manipulation of large datasets possible, we use hardware-accelerated graphics and extend a texture-based implementation of the FDEB algorithm (Wu et al, 2015 ). Our implementation harnesses the computational power of the graphical processing unit (GPU) in order to perform the required computations, and is at least 50 times faster than its CPU counterpart, enabling real-time edge bundling of over 1,000 edges at interactive rates (tested on a desktop computer with the following hardware: Intel Core i7, 3.4 GHz CPU, a Nvidia GTX 1070 GPU card, and 32GB RAM).…”

Section: Discussionmentioning

confidence: 99%

“…Standard implementations of edge bundling are too slow for the large numbers of edges that can appear in some connectome datasets visualized in NeuroCave, reducing the frame rate of the application and preventing an effective real-time experience. Therefore, we introduce an enhanced WebGL texture-based implementation, extending previous work by Wu, Yu, & Yu ( 2015 ). In our approach, we can increase the maximum number of edges between nodes through the use of multiple GPU textures.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

Keiriz

Zhan

Ajilore

et al. 2018

Network Neuroscience

View full text Add to dashboard Cite

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome—either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)—in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

Keiriz

Zhan

Ajilore

et al. 2018

Network Neuroscience

View full text Add to dashboard Cite

show abstract

“…Although this algorithm can handle graphs containing millions of edges, a complex modeling process is necessary before the edge bundling step. Compared with the MINGLE algorithm, Zhu et al [26] and Wu et al [27] demonstrated a different approach to execute FDEB, in which they leveraged GPUs to accelerate computing. Tuyishime [28] proposed a distributed edge bundling for large graphs that was able to tackle graphs that were not stored in a single machine.…”

Section: B Edge Bundlingmentioning

confidence: 99%

Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout

Luo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Origin-destination (OD) flows have time-varying characteristics and spatial heterogeneity. With the increasing amount of urban travel data, it is very challenging to clarify OD flows through traditional visualization methods. A novel approach based on the force-directed edge bundling (FDEB) algorithm is proposed to visualize OD flows and identify the main corridors in a city. First, we reduce the chaos of OD flows through vertex clustering, and then we use a modified FDEB algorithm to clarify the OD flows. Furthermore, to illustrate the validity of our approach from the perspective of traffic-representation ability, we design three evaluation metrics: local feature richness(LFR) and strength of spatial relationship (SSR), which measure the ability to express the spatial heterogeneity of OD flows, and time characteristic richness (TCR), which measures the ability to express time-varying characteristics in OD flows. Experiments are conducted on real-world automatic vehicle identification (AVI) data that are gathered from Xuancheng, China. The results show that our method can well enhance the representation of spatial heterogeneity and uncover the temporal characteristics hidden in OD flows. INDEX TERMS Edge bundling, OD flow data, traffic desire lines, visualization evaluation.

show abstract

“…4h,k,l) further enhances KDEEB and ADEB by proposing a far more efficient density estimation, also implemented on the GPU, making it possible for the first time to bundle sets of up to a million trails at interactive framerates [vdZCT16]. Separately, Texture Edge Bundling (TEB) proposes a GPU-based implementation making heavy use of texture synthesis and processing, optimized for web access [WYY15]. Lastly, Fast Fourier Transform Edge Bundling (FFTEB, Fig.…”

Section: Static Trail-setsmentioning

confidence: 99%

State of the Art in Edge and Trail Bundling Techniques

Lhuillier

Hurter

Telea

2017

Computer Graphics Forum

View full text Add to dashboard Cite

Bundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data‐based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high‐level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above‐mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.

show abstract

Texture-based edge bundling: A web-based approach for interactively visualizing large graphs

Cited by 12 publications

References 10 publications

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout

State of the Art in Edge and Trail Bundling Techniques

Contact Info

Product

Resources

About