Multi-level Graph Drawing using Infomap Clustering
Data Structures and Algorithms
2019-08-23 v1 Graphics
Abstract
Infomap clustering finds the community structures that minimize the expected description length of a random walk trajectory; algorithms for infomap clustering run fast in practice for large graphs. In this paper we leverage the effectiveness of Infomap clustering combined with the multi-level graph drawing paradigm. Experiments show that our new Infomap based multi-level algorithm produces good visualization of large and complex networks, with significant improvement in quality metrics.
Cite
@article{arxiv.1908.08151,
title = {Multi-level Graph Drawing using Infomap Clustering},
author = {Seok-Hee Hong and Peter Eades and Marnijati Torkel and Ziyang Wang and David Chae and Sungpack Hong and Daniel Langerenken and Hassan Chafi},
journal= {arXiv preprint arXiv:1908.08151},
year = {2019}
}
Comments
Appears in the Proceedings of the 27th International Symposium on Graph Drawing and Network Visualization (GD 2019)