11 research outputs found
Evaluating the readability of force directed graph layouts: A deep learning approach
Existing graph layout algorithms are usually not able to optimize all the
aesthetic properties desired in a graph layout. To evaluate how well the
desired visual features are reflected in a graph layout, many readability
metrics have been proposed in the past decades. However, the calculation of
these readability metrics often requires access to the node and edge
coordinates and is usually computationally inefficient, especially for dense
graphs. Importantly, when the node and edge coordinates are not accessible, it
becomes impossible to evaluate the graph layouts quantitatively. In this paper,
we present a novel deep learning-based approach to evaluate the readability of
graph layouts by directly using graph images. A convolutional neural network
architecture is proposed and trained on a benchmark dataset of graph images,
which is composed of synthetically-generated graphs and graphs created by
sampling from real large networks. Multiple representative readability metrics
(including edge crossing, node spread, and group overlap) are considered in the
proposed approach. We quantitatively compare our approach to traditional
methods and qualitatively evaluate our approach using a case study and
visualizing convolutional layers. This work is a first step towards using deep
learning based methods to evaluate images from the visualization field
quantitatively.Comment: This work has been accepted at IEEE CG&
A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
Inspired by the great success of machine learning (ML), researchers have
applied ML techniques to visualizations to achieve a better design,
development, and evaluation of visualizations. This branch of studies, known as
ML4VIS, is gaining increasing research attention in recent years. To
successfully adapt ML techniques for visualizations, a structured understanding
of the integration of ML4VISis needed. In this paper, we systematically survey
88 ML4VIS studies, aiming to answer two motivating questions: "what
visualization processes can be assisted by ML?" and "how ML techniques can be
used to solve visualization problems?" This survey reveals seven main processes
where the employment of ML techniques can benefit visualizations:Data
Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS
Interaction, VIS Reading, and User Profiling. The seven processes are related
to existing visualization theoretical models in an ML4VIS pipeline, aiming to
illuminate the role of ML-assisted visualization in general
visualizations.Meanwhile, the seven processes are mapped into main learning
tasks in ML to align the capabilities of ML with the needs in visualization.
Current practices and future opportunities of ML4VIS are discussed in the
context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are
still needed in the area of ML4VIS, we hope this paper can provide a
stepping-stone for future exploration. A web-based interactive browser of this
survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table