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.
CITATION STYLE
Hong, S. H., Eades, P., Torkel, M., Wang, Z., Chae, D., Hong, S., … Chafi, H. (2019). Multi-level Graph Drawing Using Infomap Clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11904 LNCS, pp. 139–146). Springer. https://doi.org/10.1007/978-3-030-35802-0_11
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