@inproceedings{7a7edb9250294701b4f6d8e546c55c42,
title = "MLSEB: Edge bundling using moving least squares approximation",
abstract = "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.",
keywords = "Edge bundling, Graph visualization, Moving least squares, Visualization quality",
author = "Jieting Wu and Jianping Zeng and Feiyu Zhu and Hongfeng Yu",
note = "Funding Information: Acknowledgment. This research has been sponsored by the National Science Foundation through grants IIS-1652846, IIS-1423487, and ICER-1541043. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 25th International Symposium on Graph Drawing and Network Visualization, GD 2017 ; Conference date: 25-09-2017 Through 27-09-2017",
year = "2018",
doi = "10.1007/978-3-319-73915-1_30",
language = "English (US)",
isbn = "9783319739144",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "379--393",
editor = "Kwan-Liu Ma and Fabrizio Frati",
booktitle = "Graph Drawing and Network Visualization - 25th International Symposium, GD 2017, Revised Selected Papers",
}