Aguilar, Luis MiguelAl-Tarawneh, RagaadHumayoun, Shah RukhDiehl, AlexandraKucher, KostiantynMédoc, Nicolas2025-05-262025-05-262025978-3-03868-286-8https://doi.org/10.2312/evp.20251137https://diglib.eg.org/handle/10.2312/evp20251137The growing volume and complexity of data from sources like social media, IoT systems, and security devices highlight the need for scalable, high-performance visualization tools. Traditional web technologies such as SVG and Canvas often struggle with large datasets, limiting interactivity. We present WebGraphViz, a WebGL-based graph visualization tool that leverages GPU parallelism to overcome these limitations. Performance evaluations across three interaction experiments in both high- and low-performance environments show that WebGraphViz significantly outperforms its SVG-based counterpart, enabling smooth exploration of large-scale graph data.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Graph drawings; Information systems → Graph-based database modelsHuman centered computing → Graph drawingsInformation systems → Graphbased database models10.2312/evp.202511373 pages