Network Visualization

Network visualization aims to represent complex relationships between entities as easily interpretable visual structures, facilitating knowledge discovery and analysis. Current research focuses on developing scalable algorithms, such as those leveraging graph neural networks (GNNs) and hierarchical approaches, to handle increasingly large and dynamic networks, often incorporating techniques like stress minimization and dimensionality reduction (e.g., t-SNE). These advancements enable effective visualization of diverse data types, from academic collaborations and news events to dynamic social and biological networks, improving understanding of complex systems across various scientific disciplines. The resulting insights can enhance knowledge extraction, support decision-making, and accelerate scientific progress.