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 and Pawar, S and Goodman, DFM},
title = {Graph Drawing by Stochastic Gradient Descent},
url = {},

RIS format (EndNote, RefMan)

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 present variousreal-world applications to show how the unique properties of SGD make it easierto produce constrained layouts than previous approaches. We also show how SGDcan be directly applied within the sparse stress approximation of Ortmann etal. [1], making the algorithm scalable up to large graphs.
AU - Zheng,JX
AU - Pawar,S
AU - Goodman,DFM
TI - Graph Drawing by Stochastic Gradient Descent
UR -
ER -