BigGraphVis: Visualizing Communities in Big Graphs Leveraging GPU-Accelerated Streaming Algorithms

Ehsan Moradi, Debajyoti Mondal

2023

Abstract

Graph layouts are key to exploring massive graphs. Motivated by the advances in streaming community detection methods that process the edge list in one pass with only a few operations per edge, we examine whether they can be leveraged to rapidly create a coarse visualization of the graph communities, and if so, then how the quality would compare with the layout of the whole graph. We introduce BigGraphVis which combines a parallelized streaming community detection algorithm and probabilistic data structure to leverage the parallel processing power of GPUs to visualize graph communities. To the best of our knowledge, this is the first attempt to combine the potential of streaming algorithms coupled with GPU computing to tackle community visualization challenges in big graphs. Our method extracts community information in a few passes on the edge list, and renders the community structures using a widely used ForceAtlas2 algorithm. The coarse layout generation process of BigGraphVis is 70 to 95 percent faster than computing a GPU-accelerated ForceAtlas2 layout of the whole graph. Our experimental results show that BigGraphVis can produce meaningful layouts, and thus opens up future opportunities to design streaming algorithms that achieve a significant computational speed up for massive networks by carefully trading off the layout quality.

Paper Citation

in Harvard Style

Moradi E. and Mondal D. (2023). BigGraphVis: Visualizing Communities in Big Graphs Leveraging GPU-Accelerated Streaming Algorithms. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP; ISBN 978-989-758-634-7, SciTePress, pages 195-202. DOI: 10.5220/0011783700003417

in Bibtex Style

@conference{ivapp23,
author={Ehsan Moradi and Debajyoti Mondal},
title={BigGraphVis: Visualizing Communities in Big Graphs Leveraging GPU-Accelerated Streaming Algorithms},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP},
year={2023},
pages={195-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011783700003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP
TI - BigGraphVis: Visualizing Communities in Big Graphs Leveraging GPU-Accelerated Streaming Algorithms
SN - 978-989-758-634-7
AU - Moradi E.
AU - Mondal D.
PY - 2023
SP - 195
EP - 202
DO - 10.5220/0011783700003417
PB - SciTePress