scholarly journals Multilayer graph edge bundling

2016 ◽  
Author(s):  
Romain Bourqui ◽  
Dino Ienco ◽  
Arnaud Sallaberry ◽  
Pascal Poncelet
Keyword(s):  
Edge Bundling

Understanding data dimensions by cluster visualization using edge bundling in parallel coordinates

2018 ◽  
Author(s):  
Rodrigo Santos do Amor Divino Lima ◽  
Carlos Gustavo Resque dos Santos ◽  
Sandro de Paula Mendonça ◽  
Jefferson Magalhães de Morais ◽  
Bianchi Serique Meiguins
Keyword(s):  
Parallel Coordinates ◽  
Cluster Visualization ◽  
Edge Bundling

scholarly journals VizTract: Visualization of Complex Social Networks for Easy User Perception

2019 ◽  
Vol 3 (1) ◽  
pp. 17
Author(s):  
Ramya Akula ◽  
Ivan Garibay
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Visual Perception ◽  
Visual Information ◽  
Circle Packing ◽  
User Friendliness ◽  
The Social ◽  
Cluster Dendrogram ◽  
Edge Bundling ◽  
Visualization Techniques

Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.

scholarly journals Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations

2020 ◽  
Vol 26 (1) ◽  
pp. 687-696 ◽  
Author(s):  
Yunhai Wang ◽  
Mingliang Xue ◽  
Yanyan Wang ◽  
Xinyuan Yan ◽  
Baoquan Chen ◽  
...  
Keyword(s):  
Edge Bundling

Interactive 3D Force-Directed Edge Bundling

2016 ◽  
Vol 35 (3) ◽  
pp. 51-60 ◽  
Author(s):  
D. Zielasko ◽  
B. Weyers ◽  
B. Hentschel ◽  
T. W. Kuhlen
Keyword(s):  
Directed Edge ◽  
Interactive 3D ◽  
Edge Bundling

Untangling origin-destination flows in geographic information systems

2017 ◽  
Vol 18 (1) ◽  
pp. 153-172 ◽  
Author(s):  
Anita Graser ◽  
Johanna Schmidt ◽  
Florian Roth ◽  
Norbert Brändle
Keyword(s):  
Information System ◽  
Information Systems ◽  
Geographic Information System ◽  
Geographic Information Systems ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sets ◽  
Flow Maps ◽  
Local Strength ◽  
Edge Bundling

Origin–destination flow maps are a popular option to visualize connections between different spatial locations, where specific routes between the origin and destination are unknown or irrelevant. Visualizing origin–destination flows is challenging mainly due to visual clutter which appears quickly as data sets grow. Clutter reduction techniques are intensively explored in the information visualization and cartography domains. However, current automatic techniques for origin–destination flow visualization, such as edge bundling, are not available in geographic information systems which are widely used to visualize spatial data, such as origin–destination flows. In this article, we explore the applicability of edge bundling to spatial data sets and necessary adaptations under the constraints inherent to platform-independent geographic information system scripting environments. We propose (1) a new clustering technique for origin–destination flows that provides within-cluster consistency to speed up computations, (2) an edge bundling approach based on force-directed edge bundling employing matrix computations, (3) a new technique to determine the local strength of a bundle leveraging spatial indexes, and (4) a geographic information system–based technique to spatially offset bundles describing different flow directions. Finally, we evaluate our method by applying it to origin–destination flow data sets with a wide variety of different data characteristics.

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