Pre-processing noisy labels with a lightweight particle-based algorithm before GCN training significantly improves robustness to label corruption while being faster than other robust methods.
This paper proposes PCC+GCN, a method that cleans noisy labels in graph data before training a Graph Convolutional Network. It uses a particle-based algorithm to identify and fix mislabeled nodes, then trains GCN on the refined labels. The approach is faster and more accurate than existing robust GCN methods across multiple datasets with different types of label noise.