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.

中文翻译：

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随机梯度下降非常适合应力最小化

应力最小化是研究最好的力导向图布局方法之一，因为它可靠地产生高质量的布局。因此，令人惊讶的是，一种基于随机梯度下降的新方法（Zheng、Pawar 和 Goodman，TVCG 2019）据称可以改进基于专业化的最新方法。我们提供的实验证据表明，新方法实际上并没有产生更好的布局，但它仍然是首选，因为它更简单、更健壮，可以抵抗不良的初始化。