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Proceedings/Recueil Des Communications Année : 2022

Deep Neural Network for DrawiNg Networks, (DNN) 2

Deep Neural Network for DrawiNg Networks, (DNN) 2

Résumé

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 framework called (DNN) 2 : Deep Neural Network for DrawiNg Networks. Our method uses Graph Convolution Networks to learn a model. Learning is achieved by optimizing a graph topology related loss function that evaluates (DNN) 2 generated layouts during training. Once trained, the (DNN) 2 model is able to quickly lay any input graph out. We experiment (DNN) 2 and statistically compare it to optimization-based and regular graph layout algorithms. The results show that (DNN) 2 performs well and are encouraging as the Deep Learning approach to Graph Drawing is novel and many leads for future works are identified.
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Dates et versions

hal-03526866 , version 1 (17-01-2022)

Identifiants

Citer

Loann Giovannangeli, Frédéric Lalanne, David Auber, Romain Giot, Romain Bourqui. Deep Neural Network for DrawiNg Networks, (DNN) 2. 12868, Springer International Publishing, pp.375-390, 2022, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-92931-2_27⟩. ⟨hal-03526866⟩

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