Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various complicated tasks such as graph classification and edge prediction. Eventually, they reached the Graph Drawing (GD) task. This article is an extended version of the previously published(DNN)2and presents a framework to leverage DL techniques for graph drawing (DL4GD). We demonstrate how it is possible to train a Deep Learning model to extract features from a graph and project them into a graph layout. The method proposes to leverage efficient Convolutional Neural Networks, adapting them to graphs using Graph Convolutions. The graph layout projection is learned by optimizing a cost function that does not require any ground truth layout, as opposed to prior work. This paper also proposes an implementation and benchmark of the framework to study its sensitivity to certain deep learning-related conditions. As the field is novel, and many questions remain to be answered, we do not focus on finding the most optimal implementation of the method, but rather contribute toward a better understanding of the approach potential. More precisely, we study different learning strategies relative to the models training datasets. Finally, we discuss the main advantages and limitations of DL4GD.

In 2D visualizations, visibility of every datum's representation is crucial to ease the completion of visual tasks. Such a guarantee is barely respected in complex visualizations, mainly because of overdraws between datum representations that hide parts of the information (e.g., outliers). The literature proposes various Layout Adjustment algorithms to improve the readability of visualizations that suffer from this issue. Manipulating the data in high-dimensional, geometric or visual space; they rely on different strategies with their own strengths and weaknesses. Moreover, most of these algorithms are computationally expensive as they search for an exact solution in the geometric space and do not scale well to large datasets. This article proposes GIST, a layout adjustment algorithm that aims at optimizing three criteria: (i) node visibility guarantee (at least 1 pixel), (ii) node size maximization, and (iii) the original layout preservation. This is achieved by combining a search for the maximum node size that enables to draw all the data points without overlaps, with a limited budget of movements (i.e., limiting the distortions of the original layout). The method's basis relies on the idea that it is not necessary for two data representations to be strictly not overlapping in order to guarantee their visibility in visual space. Our algorithm therefore uses a tolerance in the geometric space to determine the overlaps between pairs of data. The tolerance is optimized such that the approximation computed in the geometric space can lead to visualization without noticeable overdraw after the data rendering rasterization. In addition, such an approximation helps to ease the algorithm's convergence as it reduces the number of constraints to resolve, enabling it to handle large datasets. We demonstrate the effectiveness of our approach by comparing its results to those of state-of-the-art methods on several large datasets.

In many 2D visualizations, data points are projected without considering their surface area, although they are often represented as shapes in visualization tools. These shapes support the display of information such as labels or encode data with size or color. However, inappropriate shape and size selections can lead to overlaps that obscure information and hinder the visualization's exploration. Overlap Removal (OR) algorithms have been developed as a layout post-processing solution to ensure that the visible graphical elements accurately represent the underlying data. As the original data layout contains vital information about its topology, it is essential for OR algorithms to preserve it as much as possible. This article presents an extension of the previously published FORBID algorithm by introducing a new approach that models OR as a joint stress and scaling optimization problem, utilizing efficient stochastic gradient descent. The goal is to produce an overlap-free layout that proposes a compromise between compactness (to ensure the encoded data is still readable) and preservation of the original layout (to preserve the structures that convey information about the data). Additionally, this article proposes SORDID, a shape-aware adaptation of FORBID that can handle the OR task on data points having any polygonal shape. Our approaches are compared against state-of-the-art algorithms, and several quality metrics demonstrate their effectiveness in removing overlaps while retaining the compactness and structures of the input layouts.

Visualization of hierarchical weighted and directed graphs are usually done with node-link or adjacency matrix diagrams. However, these representations suffer from various drawbacks: low readability in a context of Big Data, high number of edge crossings, difficulty to efficiently represent the weighting. With the stated goal of reducing these drawbacks, we designed Adjasankey, a hybrid visual representation of weighted and directed graphs using hierarchical abstractions. This technique combines adjacency matrices readability of large graphs and flow diagrams visual design efficiency for weighting depiction. Associated to Big Data computing and light-weight web rendering, our tool allows to depict and interact in real time on huge dataset and supports user multi-scale exploration and analysis. To show the efficiency of Adjasankey, we present a case study on the analysis of a Customer to Customer website.