The rapid increase in volume and complexity of biomedical data requires changes in
research, communication, and clinical practices. This includes learning how to effectively …

In the context of data visualization and analytics, this report outlines some of the challenges
and emerging applications that arise in the Big Data era. In particularly, fourteen …

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 …

For decades, researchers in information visualisation and graph drawing have focused on
develo** techniques for the layout and display of very large and complex networks …

Node-link diagrams are widely used to facilitate network explorations. However, when using
a graph drawing technique to visualize networks, users often need to tune different algorithm …

We present new guidelines for choosing hyperparameters for t-SNE and an evaluation
comparing these guidelines to current ones. These guidelines include a proposed …

Different layouts can characterize different aspects of the same graph. Finding a “good”
layout of a graph is thus an important task for graph visualization. In practice, users often …

Numerous sampling strategies have been proposed to simplify large-scale networks for
highly readable visualizations. It is of great challenge to preserve contextual structures …

Graph or network data are widely studied in both data mining and visualization communities
to review the relationship among different entities and groups. The data facts derived from …

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 …