Here are a few applications for graph neural networks: Node classification: One of the powerful applications of GNNs is adding new information to nodes or filling gaps where information is missing.

GNN, a framework to train robust GNNs under noisy conditions. Soft-GNN mitigates label noise impact through dynamic ...

At ARVO 2025, in Salt Lake City, Utah, Patipol Tiyajamorn, talked about his poster on using graph neural networks to identify ...

Evaluate, tun, and improve the performance of the text classification models you create for your final project. In this module, we will learn about neural networks and supervised machine learning.

Examples of big graph data in the real world include complex networks, such as social networks and the Web graph, and the map graph used to represent traffic networks. The Complex Network and Map ...