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Deep Neural Network for DrawiNg Networks, (DNN)^2
[article]
2021
arXiv
pre-print
By leveraging recent progress of stochastic gradient descent methods, several works have shown that graphs could be efficiently laid out through the optimization of a tailored objective function. In the meantime, Deep Learning (DL) techniques achieved great performances in many applications. We demonstrate that it is possible to use DL techniques to learn a graph-to-layout sequence of operations thanks to a graph-related objective function. In this paper, we present a novel graph drawing
arXiv:2108.03632v2
fatcat:4k2rmxh7lrh5rfaiughnx5phnq