Results 221 to 230 of about 5,076 (252)
Some of the next articles are maybe not open access.
A Distributed Algorithm for Force Directed Edge Bundling
2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV), 2020Existing edge bundling algorithms typically require the global information structure of a graph. Therefore, with a simple division of the edges of a graph, it is challenging to conduct edge bundling in a distributed environment and achieve scalable performance. We select a representative edge bundling algorithm, Force-Directed Edge Bundling (FDEB), and
Hongfeng Yu
exaly +2 more sources
An Edge-Bundling Layout for Interactive Parallel Coordinates
2014 IEEE Pacific Visualization Symposium, 2014 Parallel Coordinates is an often used visualization method for multidimensional data sets. Its main challenges for large data sets are visual clutter and over plotting which hamper the recognition of patterns in the data. We present an edge-bundling method using density-based clustering for each dimension.
Gregorio Palmas +4 more
core +4 more sources
Evolutionary Node Layout and Edge Bundling
2022 IEEE Congress on Evolutionary Computation (CEC), 2022Ryosuke Saga
exaly +2 more sources
Towards Partially Automatic Search of Edge Bundling Parameters
Lecture Notes in Computer Science, 2018Edge bundling methods are used in flow maps and graphs to reduce the visual clutter, which is generated when representing complex and heterogeneous data. Nowadays, there are many edge bundling algorithms that have been successfully applied to a wide range of problems in graph representation. However, the majority of these methods are still difficult to
Evgheni Polisciuc +2 more
exaly +2 more sources
Visualizing large graphs by layering and bundling graph edges
The Visual Computer, 2018 Edge bundling has been widely used to reduce visual clutter and reveal high-level edge patterns for large graphs. Due to strong edge attraction, bundled results often show unnecessary curvature and tangling at bundle intersections. Inappropriate bundling may fail to reveal true data patterns and even mislead users. This paper presents a parameterizable
Zhuang Cai, Kang Zhang 0001, Dong-Ni Hu
openaire +2 more sources
Towards Unambiguous Edge Bundling: Investigating Confluent Drawings for Network Visualization
IEEE Transactions on Visualization and Computer Graphics, 2017 International audienceIn this paper, we investigate Confluent Drawings (CD), a technique for bundling edges in node-link diagrams based on network connectivity.
Benjamin Bach +2 more
exaly +2 more sources
Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations
IEEE Transactions on Visualization and Computer Graphics, 2020 International audienceMany edge bundling techniques (i.e., data simplification as a support for data visualization and decision making) exist but they are not directly applicable to any kind of dataset and their parameters are often too abstract and ...
Yunhai Wang +2 more
exaly +2 more sources
Multi-type Edge Bundling in Force-Directed Layout and Evaluation
Procedia Computer Science, 2015 Numerous information visualization techniques are available for utilizing and analyzing large data. Among these techniques, network visualization, which employs node-link diagrams, can determine the relationship among multi-dimensional data.
Ryosuke Saga
exaly +2 more sources
Research on Network Simplification by Edge Bundling
2016 IEEE First International Conference on Data Science in Cyberspace (DSC), 2016Node occlusion and edge congestion problems, which are caused by the increasing scale and complexity of network, had become a hot spot in network visualization research. To solve the visual clutter problem in network, edges close to each other in network were bundled by curving them.
Zhonghua Yao, Lingda Wu
openaire +1 more source
FFTEB: Edge bundling of huge graphs by the Fast Fourier Transform [PDF]
, 2017 Edge bundling techniques provide a visual simplification of cluttered graph drawings or trail sets. While many bundling techniques exist, only few recent ones can handle large datasets and also allow selective bundling based on edge attributes.
Antoine Lhuillier +2 more
exaly +3 more sources