Graph Regularized Encoder Training for Extreme Classification: Leveraging Graph Metadata for Enhanced Performance

The author presents RAMEN, a method that utilizes graph metadata to enhance extreme classification performance by regularizing encoder training. By replacing GCNs with non-GCN architectures, RAMEN offers significant performance boosts without increasing computational costs.