Extracting data insights and generating visual data stories from tabular data are critical parts
of data analysis. However, most existing studies primarily focus on tabular data stored as flat …

The densest subgraph problem (DSP) is of great significance due to its wide applications in
different domains. Meanwhile, diverse requirements in various applications lead to different …

The visual analysis of multivariate network data is a common yet difficult task in many
domains. The major challenge is to visualize the network's topology and additional attributes …

The visual analysis of multivariate network data is a common yet difficult task in many
domains. The major challenge is to visualize the network's topology and additional attributes …

Prior research has shown that human perception of similarity differs from mathematical
measures in visual comparison tasks, including those involving directed acyclic graphs. This …

In machine learning and data mining, causal relationship discovery is a critical task. While
the state-of-the-art Vector Auto-Regressive Linear Non-Gaussian Acyclic Model (VAR …

Recent machine learning and deep learning algorithms have made important breakthroughs
in tasks such as classification tasks. Multiple efficient methods and model architectures have …

Visualizing a graph directly via its adjacency matrix is a common and effective technique.
Such matrix visualizations rely crucially on a good ordering of the vertices to highlight …

Node-link diagrams with topology-driven layouts are effective tools for visually exploring the
structure of graphs. When exploring multivariate graphs, a frequent analytical question is to …

Existing dynamic weighted graph visualization approaches rely on users' mental
comparison to perceive temporal evolution of dynamic weighted graphs, hindering users …