Deep Neural Network for DrawiNg Networks, (DNN)^2

TL;DR

This paper introduces (DNN)^2, a novel deep learning framework using Graph Convolution Networks to efficiently generate graph layouts, demonstrating promising results compared to traditional optimization methods.

Contribution

The paper presents a new deep learning approach for graph drawing that learns to produce layouts quickly using a graph-related loss function.

Findings

(DNN)^2 performs competitively with traditional algorithms.

The method enables fast graph layout generation after training.

Deep learning offers a promising new direction for graph drawing.

Abstract

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)^ 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.