Jieting Wu, Jianxin Sun, Xinyan Xie, Tian Gao, Yu Pan, Hongfeng Yu

Abstract

We present a novel web-based framework, named Pixel-Based Edge Bundling (PBEB), for effectively and interactively visualizing large graphs. Our framework combines an image-based edge-bundling method and a parallel texture-based processing scheme, allowing us to effectively and efficiently compute edge similarities using kernel density estimation and subsequently group these edges into bundles based on their similarities. We discuss several challenges related to developing large-graph visualization on web-based platforms. To accelerate the edge bundling process and enable interactivity in web-based environments, we leverage texture-based parallel processing, a standard feature of WebGL. Our framework optimizes an end-to-end process, from bundling to rendering, enabling practical and interactive visualization of large graphs in a web-based setting. We demonstrate the superior performance of our framework by conducting comparisons with existing web-based and CUDA-based edge-bundling methods using various standard graphics cards on different devices.

Bibtex

@INPROCEEDINGS{10386295,
  author={Wu, Jieting and Sun, Jianxin and Xie, Xinyan and Gao, Tian and Pan, Yu and Yu, Hongfeng},
  booktitle={2023 IEEE International Conference on Big Data (BigData)}, 
  title={Accelerating Web-based Graph Visualization with Pixel-Based Edge Bundling}, 
  year={2023},
  volume={},
  number={},
  pages={6005-6014},
  keywords={Visualization;Image edge detection;Layout;Estimation;Data visualization;Parallel processing;Mobile handsets;graph visualization;edge bundling;similarity;GPU;WebGL},
  doi={10.1109/BigData59044.2023.10386295}
}