Graph Drawing by Stochastic Gradient Descent

Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman
10/12/2017
cs.CG

Official code: jxz12/s_gd2 (50 stars, last commit 2022-11-22).

Abstract

A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, known as stress, by using stochastic gradient descent (SGD) to move a single pair of vertices at a time. Our results show that SGD can reach lower stress levels faster and more consistently than majorization, without needing help from a good initialization. We then show how the unique properties of SGD make it easier to produce constrained layouts than previous approaches. We also show how SGD can be directly applied within the sparse stress approximation of Ortmann et al. [1], making the algorithm scalable up to large graphs.

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jxz12/s_gd2Official100%

Stress-based Graph Drawing by Stochastic Gradient Descent

506C, C#Apr 10, 20183 years ago

Cite this paper

@article{zheng2017graph,
  title  = {Graph Drawing by Stochastic Gradient Descent},
  author = {Jonathan X. Zheng and Samraat Pawar and Dan F. M. Goodman},
  year   = {2017},
  eprint = {1710.04626},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1710.04626}
}

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