Stochastic Gradient Descent Works Really Well for Stress Minimization
This addresses the graph visualization community by showing an incremental improvement in method simplicity and robustness.
The paper tackles the claim that stochastic gradient descent improves stress minimization for graph layouts, finding it does not yield better layouts but is simpler and robust to poor initialization.
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.