OpenOrd, like many layout algorithms, utilizes random seeds meaning it will yield a slightly different result each time it is run. This is an important distinction that is lost to many unfamiliar with network visualizations: there is no single "truth" to the visualized structure of a graph. Every rendering of a graph will present it in a slightly different way. Researchers frequently run a layout algorithm multiple times until they find a presentation that either looks the "best" or does the best job of visually separating the clusters of greatest importance to their analysis. What happens if we change the background color from white to black, while otherwise leaving the graph exactly as-is?
As different layouts can characterize different aspects of the same graph, finding a "good" layout of a graph is an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics.
Bicycles are quickly becoming popular for commuters in some metro areas for those who want to ditch their cars or support a green lifestyle. More than 50 cities offer some type of casual bike rental or bike-sharing program that's easy to use with the swipe of a credit card or click on an app. While it's quite the convenience for city dwellers, the companies supplying the bikes have a more complex problem: How to predict the needs of the customers and make sure there are enough bikes in any given location. In the video below, we demonstrate how to use machine learning algorithms in Oracle Data Visualization to predict expected bike rentals for a bike renting company which wants to prepare itself for the upcoming demand. The latest version of Oracle Data Visualization (v4.0) introduces a machine learning feature that lets users train or build their own machine learning models.