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 …

Graph visualizations encode relationships between objects. Abstracting the objects into
group structures provides an overview of the data. Groups can be disjoint or overlap** …

One major cause of performance degradation in predictive models is that the test samples
are not well covered by the training data. Such not well-represented samples are called OoD …

Grid visualizations are widely used in many applications to visually explain a set of data and
their proximity relationships. However, existing layout methods face difficulties when dealing …

Efficient layout of large-scale graphs remains a challenging problem: the force-directed and
dimensionality reduction-based methods suffer from high overhead for graph distance and …

Visual analytics has been widely studied in the past decade. One key to make visual
analytics practical for both research and industrial applications is the appropriate definition …

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a
network. The readability of these visualizations highly depends on the spatial layout used for …

We propose Graph Thumbnails, small icon-like visualisations of the high-level structure of
network data. Graph Thumbnails are designed to be legible in small multiples to support …

A major challenge in data‐driven biomedical research lies in the collection and
representation of data provenance information to ensure that findings are reproducibile. In …

Euler diagrams are a popular technique to visualize set‐typed data. However, creating
diagrams using simple shapes remains a challenging problem for many complex, real‐life …