Most real‐world networks are both dynamic and multivariate in nature, meaning that the
network is associated with various attributes and both the network structure and attributes …

Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …

Evaluations—encompassing computational evaluations, benchmarks and user studies—are
essential tools for validating the performance and applicability of graph and network layout …

Parallel Aggregated Ordered Hypergraph (PAOH) is a novel technique to visualize dynamic
hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several …

Temporal event sequence alignment has been used in many domains to visualize nuanced
changes and interactions overtime. Existing approaches align one or two sentinel events …

Popular social media platforms could rapidly propagate vital information over social
networks among a significant number of people. In this work we present D-Map (Diffusion …

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization
approach. However, it is vulnerable to visual clutter caused by overlap** edges, thereby …

We present the design and evaluation of HyperStorylines, a technique that generalizes
Storylines to visualize the evolution of relationships involving multiple types of entities such …

Background Understanding complicated networks of interactions and chemical components
is essential to solving contemporary problems in modern biology, especially in domains …

Dynamic networks naturally appear in a multitude of applications from different fields.
Analyzing and exploring dynamic networks in order to understand and detect patterns and …