Description

Handling noisy data is a persistent issue in machine learning, but it takes on a unique complexity in the context of graph structures. In domains where graph data is prevalent, such as social networks, biological networks, and financial systems, noisy labels can significantly degrade model performance, leading to unreliable predictions. Despite its significance, this problem is not well-explored. This competition will address this gap by investigating graph classification under label noise. We believe it could drive major advancements in both research and real-world applications.

Kaggle Notebook for the Baseline 
Our Github Page