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arXiv:2008.10376cs.CG

Stochastic Gradient Descent Works Really Well for Stress Minimization

Katharina Börsig, Ulrik Brandes, Barna Pasztor

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Abstract

Stress minimization is among the best studied force-directed graph layout methods because it reliably yields high-quality layouts. It thus comes as a surprise that a novel approach based on stochastic gradient descent (Zheng, Pawar and Goodman, TVCG 2019) is claimed to improve on state-of-the-art approaches based on majorization. We present experimental evidence that the new approach does not actually yield better layouts, but that it is still to be preferred because it is simpler and robust against poor initialization.

arXiv comments
Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)
Journal reference
DOI
Subjects
cs.CGComputational Geometry cs.SISocial and Information Networks stat.MLMachine Learning (Statistics)

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  1. Graphviz: Open-source graph visualization software 1 comment

    • Most of the work has been on CI/CD, Windows, rationalizing and modernizing the code, and Zheng, Pawar and Goodman contributed a stochastic gradient descent solver, described here, https://arxiv.org/abs/1710.04626 with benefits pointed out by Börsig, Brandes and Pasztor…

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