GNN101: Visual Learning of Graph Neural Networks in Your Web Browser

Yilin Lu, Chongwei Chen, Yuxin Chen, Kexin Huang, Marinka Zitnik, Qianwen Wang

Abstract: Graph Neural Networks (GNNs) have achieved significant success across various applications. However, their complex structures and inner workings can be challenging for non-AI experts to understand. To address this issue, this study presents GNN101, an educational visualization tool for interactive learning of GNNs. GNN101 introduces a set of animated visualizations that seamlessly integrates mathematical formulas with visualizations via multiple levels of abstraction, including a model overview, layer operations, and detailed calculations. Users can easily switch between two complementary views: a node-link view that offers an intuitive understanding of the graph data, and a matrix view that provides a space-efficient and comprehensive overview of all features and their transformations across layers. GNN101 was designed and developed based on close collaboration with four GNN experts and deployment in three GNN-related courses. We demonstrated the usability and effectiveness of GNN101 via use cases and user studies with both GNN teaching assistants and students. To ensure broad educational access, GNN101 is developed through modern web technologies and available directly in web browsers without requiring any installations.

External IDs:doi:10.1109/tvcg.2025.3634087