Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

AH igh-D imensional and I ncomplete (HDI) tensor is frequently encountered in a big data-
related application concerning the complex dynamic interactions among numerous entities …

The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

A large-scale dynamically weighted directed network (DWDN) involving numerous entities
and massive dynamic interaction is an essential data source in many big-data-related …

Graph signal processing deals with signals whose domain, defined by a graph, is irregular.
An overview of basic graph forms and definitions is presented first. Spectral analysis of …

Comics are an effective method for sequential data-driven storytelling, especially for
dynamic graphs—graphs whose vertices and edges change over time. However, manually …

Recently, hypergraph learning has shown great potential in a variety of classification tasks.
However, existing hypergraph neural networks lack flexibility in modeling and extracting …

The overview-driven visual analysis of large-scale dynamic graphs poses a major
challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze …

The rapid spread of COVID-19 disease has had a significant impact on the world. In this
paper, we study COVID-19 data interpretation and visualization using open-data sources for …

This paper introduces a novel multidimensional projection method of datasets. Our method
called Graph Regularization Multidimensional Projection (GRMP) is based on a technique …