Among recent graph drawing (GD) methods, tsNET creates high quality layouts but suffers from a very high runtime due to its underlying reliance on the t-SNE projection technique. We address this problem by presenting NNP-NET, a method that adapts NNP, a projection technique that can project high-dimensional datasets linearly in the data size, to handle both unweighted and weighted graphs, with layout quality being very close to the ground-truth tsNET. We also exploit NNP's built-in out-of-sample ability to enable NNP-NET to project time-dependent (dynamic) graphs while striking a good balance between layout stability and good layout quality. We show experiments that outline how NNP-NET can handle very large graphs - up to 50 million nodes and 108 million edges faster than all other comparable methods we are aware of while also yielding good quality metric values.

tsNET is a recent graph drawing (GD) method that creates high quality layouts but suffers from a very high runtime. We present a new GD method, NNP-NET, which reduces tsNET’s time complexity to generate layouts for very large graphs in seconds. Additionally, we extend tsNET to support drawing graphs with edge weights. We accomplish this by replacing tsNET’s t-SNE projection with Neural Network Projection (NNP), a fast dimensionality reduction (DR) method that can imitate any given DR method. Our experiments show that NNP-NET gets good quality results when compared to other state-of-the art GD methods while yielding a better computational scalability.

Graph drawings are commonly used to visualize relational data. User understanding and performance are linked to the quality of such drawings, which is measured by quality metrics. The tacit knowledge in the graph drawing community about these quality metrics is that they are not always able to accurately capture the quality of graph drawings. In particular, such metrics may rate drawings with very poor quality as very good. In this work we make this tacit knowledge explicit by showing that we can modify existing graph drawings into arbitrary target shapes while keeping one or more quality metrics almost identical. This supports the claim that more advanced quality metrics are needed to capture the "goodness" of a graph drawing and that we cannot confidently rely on the value of a single (or several) certain quality metrics.

Abstract Graph drawings using a node‐link metaphor and straight edges are widely used to represent and understand relational data. While such drawings are typically created in 2D, 3D representations have also gained popularity. When exploring 3D drawings, finding viewpoints that help understanding the graph's structure is crucial. Finding good viewpoints also allows using the 3D drawings to generate good 2D graph drawings. In this work, we tackle the problem of automatically finding high‐quality viewpoints for 3D graph drawings. We propose and evaluate strategies based on sampling, gradient descent, and evolutionary‐inspired meta‐heuristics. Our results show that most strategies quickly converge to high‐quality viewpoints within a few dozen function evaluations, with meta‐heuristic approaches showing robust performance regardless of the quality metric.

Abstract Node‐link diagrams are a widely used metaphor for creating visualizations of relational data. Most frequently, such techniques address creating 2D graph drawings, which are easy to use on computer screens and in print. In contrast, 3D node‐link graph visualizations are far less used, as they have many known limitations and comparatively few well‐understood advantages. A key issue here is that such 3D visualizations require users to select suitable viewpoints. We address this limitation by studying the ability of layout techniques to produce high‐quality views of 3D graph drawings. For this, we perform a thorough experimental evaluation, comparing 3D graph drawings, rendered from a covering sampling of all viewpoints, with their 2D counterparts across various state‐of‐the‐art node‐link drawing algorithms, graph families, and quality metrics. Our results show that, depending on the graph family, 3D node‐link diagrams can contain a many viewpoints that yield 2D visualizations that are of higher quality than those created by directly using 2D node‐link diagrams. This not only sheds light on the potential of 3D node‐link diagrams but also gives a simple approach to produce high‐quality 2D node‐link diagrams.