GraphGS: Mitigating Pseudo-Label Shift in Imbalanced Node Classification via Balanced Feature Propagation and Minority Node Selection
Graph-structured data in real-world applications often grapple with class imbalance, where underrepresented minority nodes result in biased predictions and diminished learning performance. Although pseudo-labeling methods offer a promising solution for addressing class imbalance, they remain susceptible to pseudo-label...