This work presents a survey of the current state of the art of visualization techniques for
parallel coordinates. It covers geometric models for constructing parallel coordinates and …

We present a model for building, visualizing, and interacting with multiscale representations
of information visualization techniques using hierarchical aggregation. The motivation for …

Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

Data analysis often involves the comparison of complex objects. With the ever increasing
amounts and complexity of data, the demand for systems to help with these comparisons is …

Categorical data dimensions appear in many real-world data sets, but few visualization
methods exist that properly deal with them. Parallel Sets are a new method for the …

Focus+ context visualization integrates a visually accentuated representation of selected
data items in focus (more details, more opacity, etc.) with a visually deemphasized …

Parallel coordinates have been widely applied to visualize high‐dimensional and
multivariate data, discerning patterns within the data through visual clustering. However, the …

In this paper, we present a novel parallel coordinates design integrated with points
(scattering points in parallel coordinates, SPPC), by taking advantage of both parallel …

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and
have achieved significant progress in graph analysis tasks (eg, node classification) in recent …

Typical scientific data is represented on a grid with appropriate interpolation or
approximation schemes, defined on a continuous domain. The visualization of such data in …