We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow
machine intelligence platform. This tool helps users understand complex machine learning …

The analysis of large graphs plays a prominent role in various fields of research and is
relevant in many important application areas. Effective visual analysis of graphs requires …

Many algorithms for graph layout have been devised over the last 30 years spanning both
the graph drawing and information visualisation communities. This article first reviews the …

We describe a new technique for graph layout subject to constraints. Compared to previous
techniques the proposed method is much faster and scalable to much larger graphs. For a …

We present an improved stress majorization method that incorporates various constraints,
including directional constraints without the necessity of solving a constraint optimization …

In many complex, real‐world situations, problem solving and decision making require
effective reasoning about causation and uncertainty. However, human reasoning in these …

Many different approaches have been proposed for the challenging problem of visually
analyzing large networks. Clustering is one of the most promising. In this paper, we propose …

We investigate the cognitive impact of various layout features-symmetry, alignment,
collinearity, axis alignment and orthogonality-on the recall of network diagrams (graphs) …

As a consequence of the rising interest in longitudinal social networks and their analysis,
there is also an increasing demand for tools to visualize them. We argue that similar …

Graph visualization has been widely used to understand and present both global structural
and local adjacency information in relational data sets (eg, transportation networks, citation …