Geometry-Based Edge Clustering for Graph Visualization
By Weiwei Cui, Hong Zhou, Huamin Qu, P. C. Wong, Xiaoming Li. 2008
347 citations recorded by the source corpus.
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Citations in the corpus
- Towards Better Analysis of Deep Convolutional Neural Networks (2016, methodology): “In order to reduce visual clutter, we tried the geometry-based edge bundling method [14, 25, 39] to cluster the edges between two layers.”
- Design Considerations for Optimizing Storyline Visualizations (2012, methodology): “Although some techniques, such as edge dispersing [36] and edge clustering [7, 30], significantly enhance the readability of graphs, these techniques are destructive with respect to the visibility and traceability of individual lines and therefore are less relevant to our work.”
- Graph Clustering with Graph Neural Networks (2020, background): “Furthermore, most works explore these mechanisms only within a semisupervised or supervised framework, ignoring the fact that unsupervised graph clustering is often an extremely useful end-goal in itself – whether for data exploration [44], visualization [11, 12], genomic feature discovery [7], anom”
- A survey on information visualization: recent advances and challenges (2014, background): “Among all the solutions to reduce visual clutter, edge bundling is still the most popular one [33,67, 120].”
- Online Visual Analytics of Text Streams (2015, methodology): “12, 13, and 14, the proposed streaming algo-rithm worked as well as the baseline under the three measures for adjacent tree cuts.”
- A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, methodology): “Apart from forced-based methods, recent research introduces other methods for edge bundling such as the geometry-based technique([92]) (see Fig.”