Imperial College London

Dr Dan Goodman

Faculty of EngineeringDepartment of Electrical and Electronic Engineering




+44 (0)20 7594 6264d.goodman Website




1001Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Zheng, JX-S and Pawar, S and Goodman, DFM},
doi = {10.1109/TVCG.2018.2859997},
journal = {IEEE Trans Vis Comput Graph},
title = {Graph Drawing by Stochastic Gradient Descent.},
url = {},
year = {2018}

RIS format (EndNote, RefMan)

AB - 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.
AU - Zheng,JX-S
AU - Pawar,S
AU - Goodman,DFM
DO - 10.1109/TVCG.2018.2859997
PY - 2018///
TI - Graph Drawing by Stochastic Gradient Descent.
T2 - IEEE Trans Vis Comput Graph
UR -
UR -
ER -