Edge bundling methods can effectively alleviate visual clutter and reveal high-level graph structures in large graph visualization. Researchers have devoted significant efforts to improve edge bundling according to different metrics. As the edge bundling family evolve rapidly, the quality of edge bundles receives increasing attention in the literature accordingly. In this paper, we present MLSEB, a novel method to generate edge bundles based on moving least squares (MLS) approximation. In comparison with previous edge bundling methods, we argue that our MLSEB approach can generate better results based on a quantitative metric of quality, and also ensure scalability and the efficiency for visualizing large graphs.
@article{arxiv.1709.01221,
title = {MLSEB: Edge Bundling using Moving Least Squares Approximation},
author = {Jieting Wu and Jianping Zeng and Feiyu Zhu and Hongfeng Yu},
journal= {arXiv preprint arXiv:1709.01221},
year = {2017}
}
Comments
Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017)