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Louvain-based Multi-level Graph Drawing

Cited 2 time in Web of Science Cited 1 time in Scopus
Authors

Hong, Seok-Hee; Eades, Peter; Torkel, Marnijati; Wood, James; Park, Kun Soo

Issue Date
2021-04
Publisher
IEEE
Citation
IEEE Pacific Visualization Symposium, Vol.2021-April, pp.151-155
Abstract
© 2021 IEEE.The multi-level graph drawing is a popular approach to visualize large and complex graphs. It recursively coarsens a graph and then uncoarsens the drawing using layout refinement. In this paper, we leverage the Louvain community detection algorithm for the multi-level graph drawing paradigm.More specifically, we present the Louvain-based multi-level graph drawing algorithm, and compare with other community detection algorithms such as Label Propagation and Infomap clustering. Experiments show that Louvain-based multi-level algorithm performs best in terms of efficiency (i.e., fastest runtime), while Label Propagation and Infomap-based multi-level algorithms perform better in terms of effectiveness (i.e., better visualization in quality metrics).
ISSN
2165-8765
URI
https://hdl.handle.net/10371/183748
DOI
https://doi.org/10.1109/PacificVis52677.2021.00028
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