type: string, default:
neato supports modes:
neatouses stress majorization1.
neatouses the Kamada-Kawai2 version of the gradient descent method.
KKis sometimes appreciably faster for small (number of nodes < 100) graphs. A significant disadvantage is that
neatouses a version of the Stochastic Gradient Descent3 method.
sgd’s advantage is faster and more reliable convergence than both the previous methods, while
sgd’s disadvantage is that it runs in a fixed number of iterations and may require larger values of
maxiterin some graphs.
There are two experimental modes in
mode="hier", which adds a top-down directionality similar to the layout used in
mode="ipsep", which allows the graph to specify minimum vertical and horizontal distances between nodes. (See the
sfdp, the default is
mode="spring", which corresponds to using a
spring-electrical model. Setting
mode="maxent" causes a similar model
to be run but one that also takes into account edge lengths specified by the
Gansner, E.R., Koren, Y., North, S. (2005). Graph Drawing by Stress Majorization. In: Pach, J. (eds) Graph Drawing. GD 2004. Lecture Notes in Computer Science, vol 3383. Springer, Berlin, Heidelberg. ↩︎
J. X. Zheng, S. Pawar and D. F. M. Goodman, “Graph Drawing by Stochastic Gradient Descent,” in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 9, pp. 2738-2748, 1 Sept. 2019, doi: 10.1109/TVCG.2018.2859997. ↩︎
Note: neato only.