BibTex format
@article{Zheng:2017,
author = {Zheng, JX and Pawar, S and Goodman, DFM},
title = {Graph Drawing by Stochastic Gradient Descent},
url = {http://arxiv.org/abs/1710.04626v3},
year = {2017}
}
Citation
RIS format (EndNote, RefMan)
TY - JOUR
AB - A popular method of force-directed graph drawing is multidimensional scalingusing graph-theoretic distances as input. We present an algorithm to minimizeits 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 SGDcan reach lower stress levels faster and more consistently than majorization,without needing help from a good initialization. We then show how the uniqueproperties of SGD make it easier to produce constrained layouts than previousapproaches. We also show how SGD can be directly applied within the sparsestress approximation of Ortmann et al. [1], making the algorithm scalable up tolarge graphs.
AU - Zheng,JX
AU - Pawar,S
AU - Goodman,DFM
PY - 2017///
TI - Graph Drawing by Stochastic Gradient Descent
UR - http://arxiv.org/abs/1710.04626v3
UR - http://hdl.handle.net/10044/1/62044
ER -
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